English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

There has been a long pursuit for precise and reproducible glomerularquantification on renal pathology to leverage both research and practice. Whendigitizing the biopsy tissue samples using whole slide imaging (WSI), a set ofserial 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., computedtomography) are naturally used to provide 3D context for organs, tissues, andtumors. In pathology, it is appealing to do a similar 3D assessment. However,the 3D identification and association of large-scale glomeruli on renalpathology is challenging due to large tissue deformation, missing tissues, andartifacts from WSI. In this paper, we propose a novel Multi-object Associationfor Pathology in 3D (Map3D) method for automatically identifying andassociating large-scale cross-sections of 3D objects from routine serialsectioning and WSI. The innovations of the Map3D method are three-fold: (1) thelarge-scale glomerular association is formed as a new multi-object tracking(MOT) perspective; (2) the quality-aware whole series registration is proposedto not only provide affinity estimation but also offer automatic kidney-wisequality assurance (QA) for registration; (3) a dual-path association method isproposed to tackle the large deformation, missing tissues, and artifacts duringtracking. To the best of our knowledge, the Map3D method is the first approachthat enables automatic and large-scale glomerular association across 3D serialsectioning using WSI. Our proposed method Map3D achieved MOTA= 44.6, which is12.1% higher than the non deep learning benchmarks.

Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images