Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images
Ruining Deng, Haichun Yang, Aadarsh Jha, Yuzhe Lu, Peng Chu, Agnes B., Fogo, Yuankai Huo

TL;DR
Map3D introduces a novel 3D multi-object tracking approach for associating glomeruli across serial whole slide images, improving accuracy and providing quality assurance in renal pathology analysis.
Contribution
The paper presents the first automatic large-scale glomerular association method on 3D serial WSI using a new multi-object tracking framework with quality-aware registration and dual-path association.
Findings
Achieved MOTA of 44.6, outperforming non-deep learning benchmarks by 12.1%.
Introduced a quality-aware registration method for automatic kidney-wise QA.
Developed a dual-path association technique to handle tissue deformation and artifacts.
Abstract
There has been a long pursuit for precise and reproducible glomerular quantification on renal pathology to leverage both research and practice. When digitizing the biopsy tissue samples using whole slide imaging (WSI), a set of serial sections from the same tissue can be acquired as a stack of images, similar to frames in a video. In radiology, the stack of images (e.g., computed tomography) are naturally used to provide 3D context for organs, tissues, and tumors. In pathology, it is appealing to do a similar 3D assessment. However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI. In this paper, we propose a novel Multi-object Association for Pathology in 3D (Map3D) method for automatically identifying and associating large-scale cross-sections of 3D objects from…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAI in cancer detection · Colorectal Cancer Screening and Detection · Medical Image Segmentation Techniques
