Single Dynamic Network for Multi-label Renal Pathology Image Segmentation

Ruining Deng, Quan Liu, Can Cui, Zuhayr Asad, Haichun and Yang, Yuankai Huo
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:304-314, 2022.

Abstract

Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination. The innovations have been largely focused on cancer pathology (i.e., tumor segmentation and characterization). In non-cancer pathology, the learning algorithms can be asked to examine more comprehensive tissue types simultaneously, as a multi-label setting. The prior arts typically needed to train multiple segmentation networks in order to match the domain-specific knowledge for heterogeneous tissue types (e.g., glomerular tuft, glomerular unit, proximal tubular, distal tubular, peritubular capillaries, and arteries). In this paper, we propose a dynamic single segmentation network (Omni-Seg) that learns to segment multiple tissue types using partially labeled images (i.e., only one tissue type is labeled for each training image) for renal pathology. By learning from  150,000 patch-wise pathological images from six tissue types, the proposed Omni-Seg network achieved superior segmentation accuracy and less resource consumption when compared to the previous the multiple-network and multi-head design. In the testing stage, the proposed method obtains “completely labeled" tissue segmentation results using only “partially labeled" training images. The source code is available at \url{https://github.com/ddrrnn123/Omni-Seg}

Cite this Paper

BibTeX
@InProceedings{pmlr-v172-deng22a, title = {Single Dynamic Network for Multi-label Renal Pathology Image Segmentation}, author = {Deng, Ruining and Liu, Quan and Cui, Can and Asad, Zuhayr and and Yang, Haichun and Huo, Yuankai}, booktitle = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning}, pages = {304--314}, year = {2022}, editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi}, volume = {172}, series = {Proceedings of Machine Learning Research}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v172/deng22a/deng22a.pdf}, url = {https://proceedings.mlr.press/v172/deng22a.html}, abstract = {Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination. The innovations have been largely focused on cancer pathology (i.e., tumor segmentation and characterization). In non-cancer pathology, the learning algorithms can be asked to examine more comprehensive tissue types simultaneously, as a multi-label setting. The prior arts typically needed to train multiple segmentation networks in order to match the domain-specific knowledge for heterogeneous tissue types (e.g., glomerular tuft, glomerular unit, proximal tubular, distal tubular, peritubular capillaries, and arteries). In this paper, we propose a dynamic single segmentation network (Omni-Seg) that learns to segment multiple tissue types using partially labeled images (i.e., only one tissue type is labeled for each training image) for renal pathology. By learning from  150,000 patch-wise pathological images from six tissue types, the proposed Omni-Seg network achieved superior segmentation accuracy and less resource consumption when compared to the previous the multiple-network and multi-head design. In the testing stage, the proposed method obtains “completely labeled" tissue segmentation results using only “partially labeled" training images. The source code is available at \url{https://github.com/ddrrnn123/Omni-Seg}} }
Endnote
%0 Conference Paper %T Single Dynamic Network for Multi-label Renal Pathology Image Segmentation %A Ruining Deng %A Quan Liu %A Can Cui %A Zuhayr Asad %A Haichun and Yang %A Yuankai Huo %B Proceedings of The 5th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2022 %E Ender Konukoglu %E Bjoern Menze %E Archana Venkataraman %E Christian Baumgartner %E Qi Dou %E Shadi Albarqouni %F pmlr-v172-deng22a %I PMLR %P 304--314 %U https://proceedings.mlr.press/v172/deng22a.html %V 172 %X Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination. The innovations have been largely focused on cancer pathology (i.e., tumor segmentation and characterization). In non-cancer pathology, the learning algorithms can be asked to examine more comprehensive tissue types simultaneously, as a multi-label setting. The prior arts typically needed to train multiple segmentation networks in order to match the domain-specific knowledge for heterogeneous tissue types (e.g., glomerular tuft, glomerular unit, proximal tubular, distal tubular, peritubular capillaries, and arteries). In this paper, we propose a dynamic single segmentation network (Omni-Seg) that learns to segment multiple tissue types using partially labeled images (i.e., only one tissue type is labeled for each training image) for renal pathology. By learning from  150,000 patch-wise pathological images from six tissue types, the proposed Omni-Seg network achieved superior segmentation accuracy and less resource consumption when compared to the previous the multiple-network and multi-head design. In the testing stage, the proposed method obtains “completely labeled" tissue segmentation results using only “partially labeled" training images. The source code is available at \url{https://github.com/ddrrnn123/Omni-Seg}
APA
Deng, R., Liu, Q., Cui, C., Asad, Z., and Yang, H. & Huo, Y.. (2022). Single Dynamic Network for Multi-label Renal Pathology Image Segmentation. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 172:304-314 Available from https://proceedings.mlr.press/v172/deng22a.html.

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