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PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images Med. Image Anal. (IF 10.7) Pub Date : 2024-09-21 Jieyun Bai, Zihao Zhou, Zhanhong Ou, Gregor Koehler, Raphael Stock, Klaus Maier-Hein, Marawan Elbatel, Robert Martí, Xiaomeng Li, Yaoyang Qiu, Panjie Gou, Gongping Chen, Lei Zhao, Jianxun Zhang, Yu Dai, Fangyijie Wang, Guénolé Silvestre, Kathleen Curran, Hongkun Sun, Jing Xu, Pengzhou Cai, Lu Jiang, Libin Lan, Dong Ni, Mei Zhong, Gaowen Chen, Víctor M. Campello, Yaosheng Lu, Karim Lekadir
Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time-
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Fourier Convolution Block with global receptive field for MRI reconstruction Med. Image Anal. (IF 10.7) Pub Date : 2024-09-20 Haozhong Sun, Yuze Li, Zhongsen Li, Runyu Yang, Ziming Xu, Jiaqi Dou, Haikun Qi, Huijun Chen
Reconstructing images from under-sampled Magnetic Resonance Imaging (MRI) signals significantly reduces scan time and improves clinical practice. However, Convolutional Neural Network (CNN)-based methods, while demonstrating great performance in MRI reconstruction, may face limitations due to their restricted receptive field (RF), hindering the capture of global features. This is particularly crucial
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Re-identification from histopathology images Med. Image Anal. (IF 10.7) Pub Date : 2024-09-19 Jonathan Ganz, Jonas Ammeling, Samir Jabari, Katharina Breininger, Marc Aubreville
In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can
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Maxillofacial bone movements-aware dual graph convolution approach for postoperative facial appearance prediction Med. Image Anal. (IF 10.7) Pub Date : 2024-09-19 Xinrui Huang, Dongming He, Zhenming Li, Xiaofan Zhang, Xudong Wang
Postoperative facial appearance prediction is vital for surgeons to make orthognathic surgical plans and communicate with patients. Conventional biomechanical prediction methods require heavy computations and time-consuming manual operations which hamper their clinical practice. Deep learning based methods have shown the potential to improve computational efficiency and achieve comparable accuracy
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Fetal body organ T2* relaxometry at low field strength (FOREST) Med. Image Anal. (IF 10.7) Pub Date : 2024-09-19 Kelly Payette, Alena U. Uus, Jordina Aviles Verdera, Megan Hall, Alexia Egloff, Maria Deprez, Raphaël Tomi-Tricot, Joseph V. Hajnal, Mary A. Rutherford, Lisa Story, Jana Hutter
Fetal Magnetic Resonance Imaging (MRI) at low field strengths is an exciting new field in both clinical and research settings. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artifacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing
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UM-Net: Rethinking ICGNet for polyp segmentation with uncertainty modeling Med. Image Anal. (IF 10.7) Pub Date : 2024-09-19 Xiuquan Du, Xuebin Xu, Jiajia Chen, Xuejun Zhang, Lei Li, Heng Liu, Shuo Li
Automatic segmentation of polyps from colonoscopy images plays a critical role in the early diagnosis and treatment of colorectal cancer. Nevertheless, some bottlenecks still exist. In our previous work, we mainly focused on polyps with intra-class inconsistency and low contrast, using ICGNet to solve them. Due to the different equipment, specific locations and properties of polyps, the color distribution
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Will Transformers change gastrointestinal endoscopic image analysis? A comparative analysis between CNNs and Transformers, in terms of performance, robustness and generalization Med. Image Anal. (IF 10.7) Pub Date : 2024-09-16 Carolus H.J. Kusters, Tim J.M. Jaspers, Tim G.W. Boers, Martijn R. Jong, Jelmer B. Jukema, Kiki N. Fockens, Albert J. de Groof, Jacques J. Bergman, Fons van der Sommen, Peter H.N. De With
Gastrointestinal endoscopic image analysis presents significant challenges, such as considerable variations in quality due to the challenging in-body imaging environment, the often-subtle nature of abnormalities with low interobserver agreement, and the need for real-time processing. These challenges pose strong requirements on the performance, generalization, robustness and complexity of deep learning-based
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SafeRPlan: Safe deep reinforcement learning for intraoperative planning of pedicle screw placement Med. Image Anal. (IF 10.7) Pub Date : 2024-09-16 Yunke Ao, Hooman Esfandiari, Fabio Carrillo, Christoph J. Laux, Yarden As, Ruixuan Li, Kaat Van Assche, Ayoob Davoodi, Nicola A. Cavalcanti, Mazda Farshad, Benjamin F. Grewe, Emmanuel Vander Poorten, Andreas Krause, Philipp Fürnstahl
Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of the anatomy. Robotic surgery systems have been proposed to improve placement accuracy. Despite remarkable advances, current robotic systems still lack advanced mechanisms for continuous updating of surgical plans during procedures
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A robust image segmentation and synthesis pipeline for histopathology Med. Image Anal. (IF 10.7) Pub Date : 2024-09-11 Muhammad Jehanzaib, Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Drew F.K. Williamson, Talha Abdullah, Kayhan Basak, Derya Demir, G. Evren Keles, Kashif Zafar, Mehmet Turan
Significant diagnostic variability between and within observers persists in pathology, despite the fact that digital slide images provide the ability to measure and quantify features much more precisely compared to conventional methods. Automated and accurate segmentation of cancerous cell and tissue regions can streamline the diagnostic process, providing insights into the cancer progression, and
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Low-dose computed tomography perceptual image quality assessment Med. Image Anal. (IF 10.7) Pub Date : 2024-09-06 Wonkyeong Lee, Fabian Wagner, Adrian Galdran, Yongyi Shi, Wenjun Xia, Ge Wang, Xuanqin Mou, Md. Atik Ahamed, Abdullah Al Zubaer Imran, Ji Eun Oh, Kyungsang Kim, Jong Tak Baek, Dongheon Lee, Boohwi Hong, Philip Tempelman, Donghang Lyu, Adrian Kuiper, Lars van Blokland, Maria Baldeon Calisto, Scott Hsieh, Minah Han, Jongduk Baek, Andreas Maier, Adam Wang, Garry Evan Gold, Jang-Hwan Choi
In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural
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Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges Med. Image Anal. (IF 10.7) Pub Date : 2024-09-05 Debesh Jha, Vanshali Sharma, Debapriya Banik, Debayan Bhattacharya, Kaushiki Roy, Steven A. Hicks, Nikhil Kumar Tomar, Vajira Thambawita, Adrian Krenzer, Ge-Peng Ji, Sahadev Poudel, George Batchkala, Saruar Alam, Awadelrahman M.A. Ahmed, Quoc-Huy Trinh, Zeshan Khan, Tien-Phat Nguyen, Shruti Shrestha, Sabari Nathan, Jeonghwan Gwak, Ritika K. Jha, Zheyuan Zhang, Alexander Schlaefer, Debotosh Bhattacharjee
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is
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Labeled-to-unlabeled distribution alignment for partially-supervised multi-organ medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-09-05 Xixi Jiang, Dong Zhang, Xiang Li, Kangyi Liu, Kwang-Ting Cheng, Xin Yang
Partially-supervised multi-organ medical image segmentation aims to develop a unified semantic segmentation model by utilizing multiple partially-labeled datasets, with each dataset providing labels for a single class of organs. However, the limited availability of labeled foreground organs and the absence of supervision to distinguish unlabeled foreground organs from the background pose a significant
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ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images Med. Image Anal. (IF 10.7) Pub Date : 2024-09-05 Ching-Wei Wang, Nabila Puspita Firdi, Tzu-Chiao Chu, Mohammad Faiz Iqbal Faiz, Mohammad Zafar Iqbal, Yifan Li, Bo Yang, Mayur Mallya, Ali Bashashati, Fei Li, Haipeng Wang, Mengkang Lu, Yong Xia, Tai-Kuang Chao
Ovarian cancer, predominantly epithelial ovarian cancer (EOC), is a global health concern due to its high mortality rate. Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and disease. Bevacizumab is a humanized monoclonal antibody, which blocks signaling in cancer, inhibits angiogenesis
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Intelligent surgical planning for automatic reconstruction of orbital blowout fracture using a prior adversarial generative network Med. Image Anal. (IF 10.7) Pub Date : 2024-09-04 Jiangchang Xu, Yining Wei, Shuanglin Jiang, Huifang Zhou, Yinwei Li, Xiaojun Chen
Orbital blowout fracture (OBF) is a disease that can result in herniation of orbital soft tissue, enophthalmos, and even severe visual dysfunction. Given the complex and diverse types of orbital wall fractures, reconstructing the orbital wall presents a significant challenge in OBF repair surgery. Accurate surgical planning is crucial in addressing this issue. However, there is currently a lack of
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Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba Med. Image Anal. (IF 10.7) Pub Date : 2024-09-03 Jiahao Huang, Liutao Yang, Fanwen Wang, Yinzhe Wu, Yang Nan, Weiwen Wu, Chengyan Wang, Kuangyu Shi, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb, Daoqiang Zhang, Guang Yang
Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown
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Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures Med. Image Anal. (IF 10.7) Pub Date : 2024-09-02 Francesco Manigrasso, Rosario Milazzo, Alessandro Sebastian Russo, Fabrizio Lamberti, Fredrik Strand, Andrea Pagnani, Lia Morra
The potential and promise of deep learning systems to provide an independent assessment and relieve radiologists’ burden in screening mammography have been recognized in several studies. However, the low cancer prevalence, the need to process high-resolution images, and the need to combine information from multiple views and scales still pose technical challenges. Multi-view architectures that combine
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Deep unfolding network with spatial alignment for multi-modal MRI reconstruction Med. Image Anal. (IF 10.7) Pub Date : 2024-08-31 Hao Zhang, Qi Wang, Jun Shi, Shihui Ying, Zhijie Wen
Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. To accelerate the whole acquisition process, MRI reconstruction of one modality from highly under-sampled k-space data with another fully-sampled reference modality is an efficient solution. However, the misalignment between modalities, which is common
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Image-based simulation of mitral valve dynamic closure including anisotropy Med. Image Anal. (IF 10.7) Pub Date : 2024-08-31 Nariman Khaledian, Pierre-Frédéric Villard, Peter E. Hammer, Douglas P. Perrin, Marie-Odile Berger
Simulation of the dynamic behavior of mitral valve closure could improve clinical treatment by predicting surgical procedures outcome. We propose here a method to achieve this goal by using the immersed boundary method. In order to go towards patient-based simulation, we tailor our method to be adapted to a valve extracted from medical image data. It includes investigating segmentation process, smoothness
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Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome Med. Image Anal. (IF 10.7) Pub Date : 2024-08-30 Szymon Płotka, Tomasz Szczepański, Paula Szenejko, Przemysław Korzeniowski, Jesús Rodriguez Calvo, Asma Khalil, Alireza Shamshirsaz, Robert Brawura-Biskupski-Samaha, Ivana Išgum, Clara I. Sánchez, Arkadiusz Sitek
Twin-to-Twin Transfusion Syndrome (TTTS) is a rare condition that affects about 15% of monochorionic pregnancies, in which identical twins share a single placenta. Fetoscopic laser photocoagulation (FLP) is the standard treatment for TTTS, which significantly improves the survival of fetuses. The aim of FLP is to identify abnormal connections between blood vessels and to laser ablate them in order
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Learning lifespan brain anatomical correspondence via cortical developmental continuity transfer Med. Image Anal. (IF 10.7) Pub Date : 2024-08-30 Lu Zhang, Zhengwang Wu, Xiaowei Yu, Yanjun Lyu, Zihao Wu, Haixing Dai, Lin Zhao, Li Wang, Gang Li, Xianqiao Wang, Tianming Liu, Dajiang Zhu
Identifying anatomical correspondences in the human brain throughout the lifespan is an essential prerequisite for studying brain development and aging. But given the tremendous individual variability in cortical folding patterns, the heterogeneity of different neurodevelopmental stages, and the scarce of neuroimaging data, it is difficult to infer reliable lifespan anatomical correspondence at finer
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Cross-view discrepancy-dependency network for volumetric medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-30 Shengzhou Zhong, Wenxu Wang, Qianjin Feng, Yu Zhang, Zhenyuan Ning
The limited data poses a crucial challenge for deep learning-based volumetric medical image segmentation, and many methods have tried to represent the volume by its subvolumes (, multi-view slices) for alleviating this issue. However, such methods generally sacrifice inter-slice spatial continuity. Currently, a promising avenue involves incorporating multi-view information into the network to enhance
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O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision Med. Image Anal. (IF 10.7) Pub Date : 2024-08-28 Kaiyan Li, Jingyuan Yang, Wenxuan Liang, Xingde Li, Chenxi Zhang, Lulu Chen, Chan Wu, Xiao Zhang, Zhiyan Xu, Yueling Wang, Lihui Meng, Yue Zhang, Youxin Chen, S. Kevin Zhou
Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies. The axial resolution of OCT is intrinsically constrained by the spectral bandwidth of the employed light source while maintaining a fixed center wavelength for a specific application. Physically extending this bandwidth faces strong limitations and requires a substantial cost. We
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VSmTrans: A hybrid paradigm integrating self-attention and convolution for 3D medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-24 Tiange Liu, Qingze Bai, Drew A. Torigian, Yubing Tong, Jayaram K. Udupa
Vision Transformers recently achieved a competitive performance compared with CNNs due to their excellent capability of learning global representation. However, there are two major challenges when applying them to 3D image segmentation: i) Because of the large size of 3D medical images, comprehensive global information is hard to capture due to the enormous computational costs. ii) Insufficient local
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Domain adaptive noise reduction with iterative knowledge transfer and style generalization learning Med. Image Anal. (IF 10.7) Pub Date : 2024-08-24 Yufei Tang, Tianling Lyu, Haoyang Jin, Qiang Du, Jiping Wang, Yunxiang Li, Ming Li, Yang Chen, Jian Zheng
Low-dose computed tomography (LDCT) denoising tasks face significant challenges in practical imaging scenarios. Supervised methods encounter difficulties in real-world scenarios as there are no paired data for training. Moreover, when applied to datasets with varying noise patterns, these methods may experience decreased performance owing to the domain gap. Conversely, unsupervised methods do not require
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Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs Med. Image Anal. (IF 10.7) Pub Date : 2024-08-24 Wei Peng, Tomas Bosschieter, Jiahong Ouyang, Robert Paul, Edith V. Sullivan, Adolf Pfefferbaum, Ehsan Adeli, Qingyu Zhao, Kilian M. Pohl
Recent advances in generative models have paved the way for enhanced generation of natural and medical images, including synthetic brain MRIs. However, the mainstay of current AI research focuses on optimizing synthetic MRIs with respect to visual quality (such as signal-to-noise ratio) while lacking insights into their relevance to neuroscience. To generate high-quality T1-weighted MRIs relevant for
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Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling Med. Image Anal. (IF 10.7) Pub Date : 2024-08-23 Selena Wang, Yiting Wang, Frederick H. Xu, Li Shen, Yize Zhao, Alzheimer’s Disease Neuroimaging Initiative
Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical organization. One fundamental question to address with structural connectivity is how to properly summarize and perform statistical inference for a group-level connectivity architecture, for instance, under different sex
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3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-23 Shizhan Gong, Yuan Zhong, Wenao Ma, Jinpeng Li, Zhao Wang, Jingyang Zhang, Pheng-Ann Heng, Qi Dou
Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and unstable, especially when dealing with tumor segmentation tasks that involve objects of small sizes, irregular shapes, and low contrast. Notably, the original
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Adaptive dynamic inference for few-shot left atrium segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-23 Jun Chen, Xuejiao Li, Heye Zhang, Yongwon Cho, Sung Ho Hwang, Zhifan Gao, Guang Yang
Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images is crucial for aiding the treatment of patients with atrial fibrillation. Few-shot learning holds significant potential for achieving accurate LA segmentation with low demand on high-cost labeled LGE CMR data and fast generalization across different centers. However, accurate LA segmentation
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Vessel-promoted OCT to OCTA image translation by heuristic contextual constraints Med. Image Anal. (IF 10.7) Pub Date : 2024-08-23 Shuhan Li, Dong Zhang, Xiaomeng Li, Chubin Ou, Lin An, Yanwu Xu, Weihua Yang, Yanchun Zhang, Kwang-Ting Cheng
Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which
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Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data Med. Image Anal. (IF 10.7) Pub Date : 2024-08-22 Sascha Jecklin, Youyang Shen, Amandine Gout, Daniel Suter, Lilian Calvet, Lukas Zingg, Jennifer Straub, Nicola Alessandro Cavalcanti, Mazda Farshad, Philipp Fürnstahl, Hooman Esfandiari
In this study, we address critical barriers hindering the widespread adoption of surgical navigation in orthopedic surgeries due to limitations such as time constraints, cost implications, radiation concerns, and integration within the surgical workflow. Recently, our work X23D showed an approach for generating 3D anatomical models of the spine from only a few intraoperative fluoroscopic images. This
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MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-22 Cheng Chen, Juzheng Miao, Dufan Wu, Aoxiao Zhong, Zhiling Yan, Sekeun Kim, Jiang Hu, Zhengliang Liu, Lichao Sun, Xiang Li, Tianming Liu, Pheng-Ann Heng, Quanzheng Li
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM’s performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is
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Editorial for the Special Issue on the 2022 Medical Imaging with Deep Learning Conference Med. Image Anal. (IF 10.7) Pub Date : 2024-08-19 Shadi Albarqouni, Christian Baumgartner, Qi Dou, Ender Konukoglu, Bjoern Menze, Archana Venkataraman
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Neural implicit surface reconstruction of freehand 3D ultrasound volume with geometric constraints Med. Image Anal. (IF 10.7) Pub Date : 2024-08-19 Hongbo Chen, Logiraj Kumaralingam, Shuhang Zhang, Sheng Song, Fayi Zhang, Haibin Zhang, Thanh-Tu Pham, Kumaradevan Punithakumar, Edmond H.M. Lou, Yuyao Zhang, Lawrence H. Le, Rui Zheng
Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise. Despite
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MPGAN: Multi Pareto Generative Adversarial Network for the denoising and quantitative analysis of low-dose PET images of human brain Med. Image Anal. (IF 10.7) Pub Date : 2024-08-17 Yu Fu, Shunjie Dong, Yanyan Huang, Meng Niu, Chao Ni, Lequan Yu, Kuangyu Shi, Zhijun Yao, Cheng Zhuo
Positron emission tomography (PET) imaging is widely used in medical imaging for analyzing neurological disorders and related brain diseases. Usually, full-dose imaging for PET ensures image quality but raises concerns about potential health risks of radiation exposure. The contradiction between reducing radiation exposure and maintaining diagnostic performance can be effectively addressed by reconstructing
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Rethinking masked image modelling for medical image representation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-17 Yutong Xie, Lin Gu, Tatsuya Harada, Jianpeng Zhang, Yong Xia, Qi Wu
Masked Image Modelling (MIM), a form of self-supervised learning, has garnered significant success in computer vision by improving image representations using unannotated data. Traditional MIMs typically employ a strategy of random sampling across the image. However, this random masking technique may not be ideally suited for medical imaging, which possesses distinct characteristics divergent from
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Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning Med. Image Anal. (IF 10.7) Pub Date : 2024-08-14 Niccolò Marini, Stefano Marchesin, Marek Wodzinski, Alessandro Caputo, Damian Podareanu, Bryan Cardenas Guevara, Svetla Boytcheva, Simona Vatrano, Filippo Fraggetta, Francesco Ciompi, Gianmaria Silvello, Henning Müller, Manfredo Atzori
The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while
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Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-13 Yinchi Zhou, Tianqi Chen, Jun Hou, Huidong Xie, Nicha C. Dvornek, S. Kevin Zhou, David L. Wilson, James S. Duncan, Chi Liu, Bo Zhou
Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability
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Enhancing the vision–language foundation model with key semantic knowledge-emphasized report refinement Med. Image Anal. (IF 10.7) Pub Date : 2024-08-13 Weijian Huang, Cheng Li, Hao Yang, Jiarun Liu, Yong Liang, Hairong Zheng, Shanshan Wang
Recently, vision–language representation learning has made remarkable advancements in building up medical foundation models, holding immense potential for transforming the landscape of clinical research and medical care. The underlying hypothesis is that the rich knowledge embedded in radiology reports can effectively assist and guide the learning process, reducing the need for additional labels. However
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Foundation models in gastrointestinal endoscopic AI: Impact of architecture, pre-training approach and data efficiency Med. Image Anal. (IF 10.7) Pub Date : 2024-08-12 Tim G.W. Boers, Kiki N. Fockens, Joost A. van der Putten, Tim J.M. Jaspers, Carolus H.J. Kusters, Jelmer B. Jukema, Martijn R. Jong, Maarten R. Struyvenberg, Jeroen de Groof, Jacques J. Bergman, Peter H.N. de With, Fons van der Sommen
Pre-training deep learning models with large data sets of natural images, such as ImageNet, has become the standard for endoscopic image analysis. This approach is generally superior to , due to the scarcity of high-quality medical imagery and labels. However, it is still unknown whether the learned features on natural imagery provide an optimal starting point for the downstream medical endoscopic
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Metadata-enhanced contrastive learning from retinal optical coherence tomography images Med. Image Anal. (IF 10.7) Pub Date : 2024-08-10 Robbie Holland, Oliver Leingang, Hrvoje Bogunović, Sophie Riedl, Lars Fritsche, Toby Prevost, Hendrik P.N. Scholl, Ursula Schmidt-Erfurth, Sobha Sivaprasad, Andrew J. Lotery, Daniel Rueckert, Martin J. Menten, PINNACLE consortium
Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific
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Diversity matters: Cross-head mutual mean-teaching for semi-supervised medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-10 Wei Li, Ruifeng Bian, Wenyi Zhao, Weijin Xu, Huihua Yang
Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Nevertheless, existing state-of-the-art (SOTA) methods encounter challenges in accurately predicting labels for the unlabeled data, giving rise to disruptive noise during training and susceptibility to erroneous information overfitting. Moreover,
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TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis Med. Image Anal. (IF 10.7) Pub Date : 2024-08-08 Chengyi Li, Yuheng Lu, Shan Yu, Yue Cui
Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings
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BrainSegFounder: Towards 3D foundation models for neuroimage segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-08-08 Joseph Cox, Peng Liu, Skylar E. Stolte, Yunchao Yang, Kang Liu, Kyle B. See, Huiwen Ju, Ruogu Fang
The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical foundation models have shown promise of superior performance with better sample efficiency. This work introduces a novel approach towards creating 3-dimensional (3D) medical foundation models for multimodal neuroimage segmentation through self-supervised
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SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects Med. Image Anal. (IF 10.7) Pub Date : 2024-08-08 Fanwei Kong, Sascha Stocker, Perry S. Choi, Michael Ma, Daniel B. Ennis, Alison L. Marsden
Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve diagnosis and treatment planning and may ultimately lead to improved outcomes. Deep learning (DL) methods have demonstrated the potential to enable efficient
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E[formula omitted]-MIL: An explainable and evidential multiple instance learning framework for whole slide image classification Med. Image Anal. (IF 10.7) Pub Date : 2024-08-06 Jiangbo Shi, Chen Li, Tieliang Gong, Huazhu Fu
Multiple instance learning (MIL)-based methods have been widely adopted to process the whole slide image (WSI) in the field of computational pathology. Due to the sparse slide-level supervision, these methods usually lack good localization on the tumor regions, leading to poor interpretability. Moreover, they lack robust uncertainty estimation of prediction results, leading to poor reliability. To
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JOSA: Joint surface-based registration and atlas construction of brain geometry and function Med. Image Anal. (IF 10.7) Pub Date : 2024-08-03 Jian Li, Greta Tuckute, Evelina Fedorenko, Brian L. Edlow, Adrian V. Dalca, Bruce Fischl
Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding
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Structure and position-aware graph neural network for airway labeling Med. Image Anal. (IF 10.7) Pub Date : 2024-08-02 Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier, Bram van Ginneken
We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks and enriched using graph neural networks. Our graph neural network is structure-aware by having each node aggregate
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Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT Med. Image Anal. (IF 10.7) Pub Date : 2024-07-31 Vatsala Sharma, Suyash P. Awate, for the
In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. For image quality enhancement in low-dose PET and CT, we propose a novel theoretical adversarial and variational deep neural network (DNN) framework relying on expectation maximization (EM) based learning, termed adversarial EM (AdvEM). AdvEM proposes
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PathoDuet: Foundation models for pathological slide analysis of H&E and IHC stains Med. Image Anal. (IF 10.7) Pub Date : 2024-07-31 Shengyi Hua, Fang Yan, Tianle Shen, Lei Ma, Xiaofan Zhang
Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet
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Image-level supervision and self-training for transformer-based cross-modality tumor segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-07-31 Malo Alefsen de Boisredon d’Assier, Aloys Portafaix, Eugene Vorontsov, William Trung Le, Samuel Kadoury
Deep neural networks are commonly used for automated medical image segmentation, but models will frequently struggle to generalize well across different imaging modalities. This issue is particularly problematic due to the limited availability of annotated data, both in the target as well as the source modality, making it difficult to deploy these models on a larger scale. To overcome these challenges
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AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking Med. Image Anal. (IF 10.7) Pub Date : 2024-07-30 Wenxuan Li, Chongyu Qu, Xiaoxi Chen, Pedro R.A.S. Bassi, Yijia Shi, Yuxiang Lai, Qian Yu, Huimin Xue, Yixiong Chen, Xiaorui Lin, Yutong Tang, Yining Cao, Haoqi Han, Zheyuan Zhang, Jiawei Liu, Tiezheng Zhang, Yujiu Ma, Jincheng Wang, Guang Zhang, Alan Yuille, Zongwei Zhou
We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually
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NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis Med. Image Anal. (IF 10.7) Pub Date : 2024-07-29 Houxiang Wang, Jiaqing Chen, Zihao Yuan, Yangxin Huang, Fuchun Lin
The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer
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Know your orientation: A viewpoint-aware framework for polyp segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-07-29 Linghan Cai, Lijiang Chen, Jianhao Huang, Yifeng Wang, Yongbing Zhang
Automatic polyp segmentation in endoscopic images is critical for the early diagnosis of colorectal cancer. Despite the availability of powerful segmentation models, two challenges still impede the accuracy of polyp segmentation algorithms. Firstly, during a colonoscopy, physicians frequently adjust the orientation of the colonoscope tip to capture underlying lesions, resulting in viewpoint changes
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SFPL: Sample-specific fine-grained prototype learning for imbalanced medical image classification Med. Image Anal. (IF 10.7) Pub Date : 2024-07-25 Yongbei Zhu, Shuo Wang, He Yu, Weimin Li, Jie Tian
Imbalanced classification is a common and difficult task in many medical image analysis applications. However, most existing approaches focus on balancing feature distribution and classifier weights between classes, while ignoring the inner-class heterogeneity and the individuality of each sample. In this paper, we proposed a sample-specific fine-grained prototype learning (SFPL) method to learn the
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Improving the radiographic image analysis of the classic metaphyseal lesion via conditional diffusion models Med. Image Anal. (IF 10.7) Pub Date : 2024-07-25 Shaoju Wu, Sila Kurugol, Andy Tsai
The classic metaphyseal lesion (CML) is a unique fracture highly specific for infant abuse. This fracture is often subtle in radiographic appearance and commonly occurs in the distal tibia. The development of an automated model that can accurately identify distal tibial radiographs with CMLs is important to assist radiologists in detecting these fractures. However, building such a model typically requires
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TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers Med. Image Anal. (IF 10.7) Pub Date : 2024-07-22 Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, Matthew P. Lungren, Shaoting Zhang, Lei Xing, Le Lu, Alan Yuille, Yuyin Zhou
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into medical image segmentation. However, a comprehensive understanding of Transformers’ self-attention in U-Net components is lacking. TransUNet, first introduced
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EfficientQ: An efficient and accurate post-training neural network quantization method for medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-07-22 Rongzhao Zhang, Albert C.S. Chung
Model quantization is a promising technique that can simultaneously compress and accelerate a deep neural network by limiting its computation bit-width, which plays a crucial role in the fast-growing AI industry. Despite model quantization’s success in producing well-performing low-bit models, the quantization process itself can still be expensive, which may involve a long fine-tuning stage on a large
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PRSCS-Net: Progressive 3D/2D rigid Registration network with the guidance of Single-view Cycle Synthesis Med. Image Anal. (IF 10.7) Pub Date : 2024-07-20 Wencong Zhang, Lei Zhao, Hang Gou, Yanggang Gong, Yujia Zhou, Qianjin Feng
The 3D/2D registration for 3D pre-operative images (computed tomography, CT) and 2D intra-operative images (X-ray) plays an important role in image-guided spine surgeries. Conventional iterative-based approaches suffer from time-consuming processes. Existing learning-based approaches require high computational costs and face poor performance on large misalignment because of projection-induced losses
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Interpretable medical image Visual Question Answering via multi-modal relationship graph learning Med. Image Anal. (IF 10.7) Pub Date : 2024-07-20 Xinyue Hu, Lin Gu, Kazuma Kobayashi, Liangchen Liu, Mengliang Zhang, Tatsuya Harada, Ronald M. Summers, Yingying Zhu
Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficiency of medical professionals while relieving the burden on the public health system, particularly in resource-poor countries. However, existing medical VQA