Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data

• URL: http://arxiv.org/abs/2112.12665v1
• Date: Thu, 23 Dec 2021 16:02:03 GMT
• Title: Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data
• Authors: Ruining Deng, Quan Liu, Can Cui, Zuhayr Asad, Haichun Yang, Yuankai Huo
• Abstract summary: In non-cancer pathology, the learning algorithms can be asked to examine more comprehensive tissue types simultaneously. The prior arts needed to train multiple segmentation networks in order to match the domain-specific knowledge. By learning from 150,000 patch-wise pathological images, the proposed Omni-Seg network achieved superior segmentation accuracy and less resource consumption.
• Score: 6.528287373027917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in precision medicine. 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 https://github.com/ddrrnn123/Omni-Seg.

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