PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

卷:231

Article

关键词: FRAMEWORK;    NOISE;   

DOI  :  10.1016/j.cmpb.2023.107398

来源: SCIE

【 摘 要 】

Background and Objective: Open-source deep learning toolkits are one of the driving forces for develop-ing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on fully supervised segmentation that assumes full and accu-rate pixel-level annotations are available. Such annotations are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the an-notation cost. We aim to develop a new deep learning toolkit to support annotation-efficient learning for medical image segmentation, which can accelerate and simplify the development of deep learning models with limited annotation budget, e.g., learning from partial, sparse or noisy annotations.Methods: Our proposed toolkit named PyMIC is a modular deep learning library for medical image seg-mentation tasks. In addition to basic components that support development of high-performance models for fully supervised segmentation, it contains several advanced components that are tailored for learn-ing from imperfect annotations, such as loading annotated and unannounced images, loss functions for unannotated, partially or inaccurately annotated images, and training procedures for co-learning between multiple networks, etc. PyMIC is built on the PyTorch framework and supports development of semi -supervised, weakly supervised and noise-robust learning methods for medical image segmentation.Results: We present several illustrative medical image segmentation tasks based on PyMIC: (1) Achieving competitive performance on fully supervised learning; (2) Semi-supervised cardiac structure segmenta-tion with only 10% training images annotated; (3) Weakly supervised segmentation using scribble anno-tations; and (4) Learning from noisy labels for chest radiograph segmentation. Conclusions: The PyMIC toolkit is easy to use and facilitates efficient development of medical image segmentation models with imperfect annotations. It is modular and flexible, which enables researchers to develop high-performance models with low annotation cost. The source code is available at: https: //github.com/HiLab-git/PyMIC .(c) 2023 Elsevier B.V. All rights reserved.

【 授权许可】

Free