Abstract
Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance. However, existing networks tend to struggle with task-specific, heavy and complex designs to improve accuracy. They made little instructions to which feature channels would be more beneficial for segmentation, and that may be why the performance and universality of these segmentation models are hindered. In this study, we propose an instructive feature enhancement approach, namely IFE, to adaptively select feature channels with rich texture cues and strong discriminability to enhance raw features based on local curvature or global information entropy criteria. Being plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model to focus on texture-rich features which are especially important for the ambiguous and challenging boundary identification, simultaneously achieving simplicity, universality, and certain interpretability. To evaluate the proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k, which contains 55,023 images from 7 modalities and 26 anatomical structures. Extensive experiments show that IFE can improve the performance of classic segmentation networks across different anatomies and modalities with only slight modifications. Code is available at https://github.com/yezi-66/IFE.
L. Liu and H. Zhou—Contributed equally to this work.
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Acknowledgements
The authors of this paper sincerely appreciate all the challenge organizers and owners for providing the public MIS datasets including AbdomenCT-1K, ACDC, AMOS 2022, BraTS20, CHAOS, CRAG, crossMoDA, EndoTect 2020, ETIS-Larib Polyp DB, iChallenge-AMD, iChallenge-PALM, IDRiD 2018, ISIC 2018, I2CVB, KiPA22, KiTS19& KiTS21, Kvasir-SEG, LUNA16, Multi-Atlas Labeling Beyond the Cranial Vault (Abdomen), Montgomery County CXR Set, M&Ms, MSD, NCI-ISBI 2013, PROMISE12, QUBIQ 2021, SIIM-ACR, SLIVER07, VerSe19 & VerSe20, Warwick-QU, and WORD.
This work was supported by the grant from National Natural Science Foundation of China (Nos. 62171290, 62101343), Shenzhen-Hong Kong Joint Research Program (No. SGDX20201103095613036), and Shenzhen Science and Technology Innovations Committee (No. 20200812143441001).
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Liu, L. et al. (2023). Instructive Feature Enhancement for Dichotomous Medical Image Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_42
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