Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation
arxiv(2023)
摘要
Renal biopsies are the gold standard for diagnosis of kidney diseases. Lesion
scores made by renal pathologists are semi-quantitative and exhibit high
inter-observer variability. Automating lesion classification within segmented
anatomical structures can provide decision support in quantification analysis
and reduce the inter-observer variability. Nevertheless, classifying lesions in
regions-of-interest (ROIs) is clinically challenging due to (a) a large amount
of densely packed anatomical objects (up to 1000), (b) class imbalance across
different compartments (at least 3), (c) significant variation in object scales
(i.e. sizes and shapes), and (d) the presence of multi-label lesions per
anatomical structure. Existing models lack the capacity to address these
complexities efficiently and generically. This paper presents a
generalized technical solution for large-scale, multi-source datasets with
diverse lesions. Our approach utilizes two sub-networks: dense instance
segmentation and lesion classification. We introduce DiffRegFormer, an
end-to-end dense instance segmentation model designed for multi-class,
multi-scale objects within ROIs. Combining diffusion models, transformers, and
RCNNs, DiffRegFormer efficiently recognizes over 500 objects across three
anatomical classes (glomeruli, tubuli, arteries) within ROIs on a single NVIDIA
GeForce RTX 3090 GPU. On a dataset of 303 ROIs (from 148 Jones' silver-stained
renal WSIs), it outperforms state of art models, achieving AP of 52.1%
(detection) and 46.8% (segmentation). Our lesion classification sub-network
achieves 89.2% precision and 64.6% recall on 21889 object patches (from the
303 ROIs). Importantly, the model demonstrates direct domain transfer to
PAS-stained WSIs without fine-tuning.
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