实验室2名硕士生(王振威，贺孟申)为第一作者的论文被MICCAI2022接收。MICCAI的全称是International Conference on Medical Image Computing and Computer Assisted Intervention，是医学图像分析领域的顶级国际会议。MICCAI，ISBI，IPMI为医学图像领域的3大顶会。MICCAI会议具有以下特点：高度国际化（134所全球顶级科研高校的世界权威研究团队）、覆盖范围广（智能化医学检测、诊断与治疗领域，聚焦热点技术、关键理论、重大疾病应用与交叉融合领域，覆盖了计算病理学、脑疾病诊断、超声成像分析、智能化手术引导等多个领域）、学科前沿交叉（不仅关注疾病诊断，更强调疾病智能化的治疗引导，如智能化的放射治疗以及基于增强现实的手术引导策略等重点领域，同时将在深度学习、迁移学习、统计图谱、域自适应等热点方向开展专题研讨）以及多元化交流等。迄今已经举办了24届。第25届MICCAI会议将于2022年9月在新加坡召开，本次会议共收到投稿1800余篇，往年的录用率通常在30%以内。

以下为2篇入选论文科研成果概述：

1）Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner

Fiber tract segmentation is a prerequisite for the tract-based statistical analysis and plays a crucial role in understanding brain structure and function. The previous researches mainly consist of two steps: defining and computing the similarity features of fibers, and then adopting machine learning algorithm for clustering or classification. Among them, how to define similarity is the basic premise and assumption of the whole method, and determines its potential reliability and application. The similarity features defined by previous studies ranged from geometric to anatomical, and then to functional characteristics, accordingly, the resulting fiber tracts seem more and more meaningful, while their reliability declined. Therefore, here we still adopt geometric feature for fiber tract segmentation, and put forward a novel descriptor (FiberGeoMap) for representing fiber’s geometric feature, which can depict effectively the shape and position of fiber, and can be inputted into our revised Transformer encoder network, called as FiberGeoMap Learner, which can well fully leverage the fiber’s features. Experimental results showed that the FiberGeoMap combined with FiberGeoMap Learner can effectively express fiber’s geometric features, and can differentiate the various fiber tracts, furthermore, the common fiber tracts among individuals can be identified by this method, thus avoiding additional image registration. The comparative experiments demonstrated that the proposed method had better performance than the existing methods. The code is openly available at https://github.com/Garand0o0/FiberTractSegmentation.

2）Multi-head Attention-based Masked Sequence Model for Mapping Functional Brain Networks

It has been of great interest in the neuroimaging community to discover brain functional networks (FBNs) based on task functional magnetic resonance imag-ing (tfMRI). A variety of methods have been used to model tfMRI sequences so far, such as recurrent neural network (RNN) and Autoencoder. However, these models are not designed to incorporate the characteristics of tfMRI sequences, and the same signal values at different time points in a fMRI time series may rep-resent different states and meanings. Inspired by cloze learning methods and the human ability to judge polysemous words based on context, we proposed a self-supervised a Multi-head Attention-based Masked Sequence Model (MAMSM), as BERT model uses (Masked Language Modeling) MLM and multi-head atten-tion to learn the different meanings of the same word in different sentences. MAMSM masks and encodes tfMRI time series, uses multi-head attention to cal-culate different meanings corresponding to the same signal value in fMRI se-quence, and obtains context information through MSM pre-training. Furthermore this work redefined a new loss function to extract FBNs according to the task de-sign information of tfMRI data. The model has been applied to the Human Con-nectome Project (HCP) task fMRI dataset and achieves state-of-the-art perfor-mance in brain temporal dynamics, the Pearson correlation coefficient between learning features and task design curves was more than 0.95, and the model can extract more meaningful network besides the known task related brain networks.