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Computer-assisted quantitative analysis on Giga-pixel pathology images hasprovided a new avenue in histology examination. The innovations have beenlargely focused on cancer pathology (i.e., tumor segmentation andcharacterization). In non-cancer pathology, the learning algorithms can beasked to examine more comprehensive tissue types simultaneously, as amulti-label setting. The prior arts typically needed to train multiplesegmentation networks in order to match the domain-specific knowledge forheterogeneous tissue types (e.g., glomerular tuft, glomerular unit, proximaltubular, distal tubular, peritubular capillaries, and arteries). In this paper,we propose a dynamic single segmentation network (Omni-Seg) that learns tosegment multiple tissue types using partially labeled images (i.e., only onetissue type is labeled for each training image) for renal pathology. Bylearning from ~150,000 patch-wise pathological images from six tissue types,the proposed Omni-Seg network achieved superior segmentation accuracy and lessresource consumption when compared to the previous the multiple-network andmulti-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 athttps://github.com/ddrrnn123/Omni-Seg

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