  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
https://github.com/ddrrnn123/Omni-Seg
