ArticleIEEE transactions on bio-medical engineering2023
Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation.
Article in IEEE transactions on bio-medical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A fused attention-based hybrid model for semi-supervised medical image segmentation.Biomedical engineering advances · 2026Article
- How Close Are We? Limitations and Progress of AI Models in Banff Lesion Scoring.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- The problem of segmenting global glomerulosclerosis in gigapixel histopathological images: the borderless glomeruli.BMC nephrology · 2025Article
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
- Learnable Context in Multiple Instance Learning for Whole Slide Image Classification and Segmentation.Journal of imaging informatics in medicine · 2025Article
- Explainable attention-enhanced heuristic paradigm for multi-view prognostic risk score development in hepatocellular carcinoma.Hepatology international · 2025Article
- Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection.Current opinion in organ transplantation · 2025Review
- Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.IS&T International Symposium on Electronic Imaging · 2025Article
- Leveraging advanced feature extraction for improved kidney biopsy segmentation.Frontiers in medicine · 2025Article
- HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- A Comprehensive Look at In Vitro Angiogenesis Image Analysis Software.International journal of molecular sciences · 2023Review
- Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology.Biomolecules · 2023Review
- An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times larger than that of the peritubular capillaries, making it impractical to segment both objects on the same patch, at the same scale. To handle this scaling issue, prior studies have typically trained multiple segmentation networks in order to match the optimal pixel resolution of heterogeneous tissue types. This multi-network solution is resource-intensive and fails to model the spatial relationship between tissue types. In this article, we propose the Omni-Seg network, a scale-aware dynamic neural network that achieves multi-object (six tissue types) and multi-scale (5× to 40× scale) pathological image segmentation via a single neural network. The contribution of this article is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining. By learning from 150,000 human pathological image patches from six tissue types at three different resolutions, our approach achieved superior segmentation performance according to human visual assessment and evaluation of image-omics (i.e., spatial transcriptomics).
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