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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| Online Access: | https://arxiv.org/abs/2309.17166 |
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| _version_ | 1866913616123396096 |
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| author | Xiong, Zhan He, Junling Valkema, Pieter Nguyen, Tri Q. Naesens, Maarten Kers, Jesper Verbeek, Fons J. |
| author_facet | Xiong, Zhan He, Junling Valkema, Pieter Nguyen, Tri Q. Naesens, Maarten Kers, Jesper Verbeek, Fons J. |
| contents | Renal biopsies are the gold standard for the 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, thereby reducing 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, (b) class imbalance across different compartments (at least 3), (c) significant variation in size and shape of anatomical objects and (d) the presence of multi-label lesions per anatomical structure. Existing models cannot address these complexities in an efficient and generic manner. This paper presents an analysis for a \textbf{generalized solution} to datasets from various sources (pathology departments) with different types of lesions. Our approach utilizes two sub-networks: dense instance segmentation and lesion classification. We introduce \textbf{DiffRegFormer}, an end-to-end dense instance segmentation sub-network designed for multi-class, multi-scale objects within ROIs. Combining diffusion models, transformers, and RCNNs, DiffRegFormer {is a computational-friendly framework that can efficiently recognize over 500 objects across three anatomical classes, i.e., glomeruli, tubuli, and arteries, within ROIs.} In a dataset of 303 ROIs from 148 Jones' silver-stained renal Whole Slide Images (WSIs), our approach outperforms previous methods, achieving an Average Precision of 52.1\% (detection) and 46.8\% (segmentation). Moreover, our lesion classification sub-network achieves 89.2\% precision and 64.6\% recall on 21889 object patches out of the 303 ROIs. Lastly, our model demonstrates direct domain transfer to PAS-stained renal WSIs without fine-tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_17166 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation Xiong, Zhan He, Junling Valkema, Pieter Nguyen, Tri Q. Naesens, Maarten Kers, Jesper Verbeek, Fons J. Computer Vision and Pattern Recognition Artificial Intelligence Renal biopsies are the gold standard for the 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, thereby reducing 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, (b) class imbalance across different compartments (at least 3), (c) significant variation in size and shape of anatomical objects and (d) the presence of multi-label lesions per anatomical structure. Existing models cannot address these complexities in an efficient and generic manner. This paper presents an analysis for a \textbf{generalized solution} to datasets from various sources (pathology departments) with different types of lesions. Our approach utilizes two sub-networks: dense instance segmentation and lesion classification. We introduce \textbf{DiffRegFormer}, an end-to-end dense instance segmentation sub-network designed for multi-class, multi-scale objects within ROIs. Combining diffusion models, transformers, and RCNNs, DiffRegFormer {is a computational-friendly framework that can efficiently recognize over 500 objects across three anatomical classes, i.e., glomeruli, tubuli, and arteries, within ROIs.} In a dataset of 303 ROIs from 148 Jones' silver-stained renal Whole Slide Images (WSIs), our approach outperforms previous methods, achieving an Average Precision of 52.1\% (detection) and 46.8\% (segmentation). Moreover, our lesion classification sub-network achieves 89.2\% precision and 64.6\% recall on 21889 object patches out of the 303 ROIs. Lastly, our model demonstrates direct domain transfer to PAS-stained renal WSIs without fine-tuning. |
| title | Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2309.17166 |