PatchSorter: a high throughput deep learning digital pathology tool for object labeling,npj Digital Medicine

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PatchSorter: a high throughput deep learning digital pathology tool for object labeling
npj Digital Medicine ( IF 12.4 ) Pub Date : 2024-06-20 , DOI: 10.1038/s41746-024-01150-4
Cédric Walker _{1,

2} , Tasneem Talawalla ₃ , Robert Toth ₄ , Akhil Ambekar _{5,

6} , Kien Rea ₃ , Oswin Chamian ₃ , Fan Fan ₃ , Sabina Berezowska ₇ , Sven Rottenberg _{1,

8} , Anant Madabhushi _{9,

10} , Marie Maillard ₇ , Laura Barisoni _{5,

11} , Hugo Mark Horlings ₁₂ , Andrew Janowczyk _{9,

13,

14}

Affiliation

Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland.
Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Toth Technology LLC, Toth Technology LLC, New Brunswick, NJ, USA.
Department of Pathology, Division of AI & Computational Pathology, Duke University, Durham, NC, USA.
AI Health, Duke University, Durham, NC, USA.
Institute of Pathology, Lausanne University Hospital, Lausanne, Switzerland.
Bern Center for Precision Medicine, University of Bern, Bern, Switzerland.
Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
Atlanta Veterans Medical Center, Atlanta, GA, USA.
Department of Medicine, Division of Nephrology, Duke University, Durham, NC, USA.
Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Department of Oncology, Division of Precision Oncology, Geneva University Hospitals, Geneva, Switzerland.
Department of Diagnostics, Division of Clinical Pathology, Geneva University Hospitals, Geneva, Switzerland.

The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

中文翻译：

PatchSorter：用于对象标记的高通量深度学习数字病理学工具

在数字病理图像中发现与诊断、预后和治疗反应相关的模式通常需要对大量组织学对象进行棘手的标记。在这里，我们发布了一个开源标签工具 PatchSorter，它将深度学习与直观的 Web 界面集成在一起。使用超过 100,000 个对象，我们证明每秒标签速度比无辅助标签提高了 7 倍以上，并且对标签准确性的影响最小，从而实现了大型数据集的高吞吐量标签。

更新日期：2024-06-21

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