# MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations

arXiv: https://arxiv.org/abs/2605.28261 · HTML: https://mlsanity.com/paper/2605.28261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 16 of 16)

- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://mlsanity.com/paper/2212.07784/) (2022)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [CBNet: A Composite Backbone Network Architecture for Object Detection](https://mlsanity.com/paper/2107.00420/) (2021)
- [PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation](https://mlsanity.com/paper/2402.19286/) (2024)
- [Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints](https://mlsanity.com/paper/2004.09821/) (2020)
- [SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation](https://mlsanity.com/paper/2303.08578/) (2023)
- [HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis](https://mlsanity.com/paper/2407.00596/) (2024)
- [KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level](https://mlsanity.com/paper/2502.07288/) (2025)
- [PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit](https://mlsanity.com/paper/2501.06151/) (2025)
- [Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology](https://mlsanity.com/paper/2308.06288/) (2023)
- [Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation](https://mlsanity.com/paper/2309.17166/) (2023)
- [DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology](https://mlsanity.com/paper/2508.15208/) (2025)
- [M^3-GloDets: Multi-Region and Multi-Scale Analysis of Fine-Grained Diseased Glomerular Detection](https://mlsanity.com/paper/2508.17666/) (2025)

## Abstract (arXiv)

Instance-level quantification of kidney functional units is essential for morphometric analysis, yet most publicly available pathology datasets provide only semantic segmentation annotations, where adjacent structures of the same class are merged into single regions. This prevents reliable instance-level analysis and limits downstream quantitative studies. Existing heuristic post-processing methods often yield suboptimal instance separation, particularly in crowded and adherent regions, while deep learning-based instance segmentation approaches typically require intensive instance-level annotations that are costly and labor-intensive to obtain. We propose MORI-Seg, a deep learning framework that enables instance segmentation without requiring instance-level annotations. Instead of heuristic splitting or instance supervision, MORI-Seg learns morphology-aware geometric representations directly from semantic masks by jointly modeling object-centric distance fields and boundary-band representations to encode interior structure and contact interfaces. A class-conditioned feature disentanglement module further promotes intra-instance coherence and inter-instance separation. Under semantic-only supervision, MORI-Seg decomposes connected semantic regions into distinct instance masks in an end-to-end manner. Experiments demonstrate improved instance separation accuracy and more reliable morphometric quantification compared with classical post-processing pipelines and representative semantic-to-instance learning approaches. The official implementation is publicly available at https://github.com/ddrrnn123/MORI-Seg.