Can few-shot learning methods segment global-sclerotic glomeruli in WSI?
Resumo
Glomeruli are central to the analysis of whole slide images (WSIs) in kidney biopsies, as they are affected by a wide range of lesions that reflect both causes and consequences of renal diseases. In automated WSI analysis using machine learning, glomeruli are typically the first regions to be segmented or detected, enabling subsequent diagnostic tasks. The Bowman’s capsule (BC) usually serves as the primary anatomical marker, delineating the glomerulus from the surrounding interstitium. While this boundary is preserved in normal and partially sclerotic glomeruli, globally sclerotic glomeruli often lose the BC, appearing visually borderless and posing a major challenge for automatic detection. In recent years, several studies have addressed glomerulus segmentation; however, few have focused on the specific challenge of globally sclerotic glomeruli, often analyzing them only in isolated per-cropped images. In this work, we present a comparative evaluation of four few-shot semantic segmentation (FSS) methods: DMACA, VAT, HSNet, and PMNet. These approaches aim to learn from only a few labeled examples, addressing the data scarcity of globally sclerotic glomeruli. These methods were applied to three classes of glomeruli: those with well-defined borders, partially borderless glomeruli, and globally sclerotic borderless glomeruli, using the Dice metric. Our results highlight the intrinsic difficulty of segmenting globally sclerotic glomeruli from WSIs, with a mean Dice score across all the evaluated methods of only 0.02 when evaluated at the wholeslide level. In contrast, per-crop evaluations yielded markedly higher performance, with mean Dice scores reaching 0.93 for globally sclerotic glomeruli.Referências
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V. Della Mea, F. Demichelis, F. Viel, P. Dalla Palma, and C. A. Beltrami, “User attitudes in analyzing digital slides in a quality control test bed: A preliminary study,” Computer Methods and Programs in Biomedicine, vol. 82, no. 2, pp. 177–186, 2006.
N. Bayramoglu, J. Kannala, and J. Heikkilä, “Deep learning for magnification independent breast cancer histopathology image classification,” in 2016 23rd International conference on pattern recognition (ICPR). IEEE, 2016, pp. 2440–2445.
M. Haas, S. V. Seshan, L. Barisoni, K. Amann, I. M. Bajema, J. U. Becker, K. Joh, D. Ljubanovic, I. S. Roberts, J. J. Roelofs et al., “Consensus definitions for glomerular lesions by light and electron microscopy: recommendations from a working group of the renal pathology society,” Kidney international, vol. 98, no. 5, pp. 1120–1134, 2020.
L. Jiang, W. Chen, B. Dong, K. Mei, C. Zhu, J. Liu, M. Cai, Y. Yan, G. Wang, L. Zuo et al., “A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images,” The American Journal of Pathology, vol. 191, no. 8, pp. 1431–1441, 2021.
A. Jha, H. Yang, R. Deng, M. E. Kapp, A. B. Fogo, and Y. Huo, “Instance segmentation for whole slide imaging: end-to-end or detectthen-segment,” Journal of Medical Imaging, vol. 8, no. 1, pp. 1–16, 2021.
C. P. Jayapandian, Y. Chen, A. R. Janowczyk, M. B. Palmer, C. A. Cassol et al., “Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains,” Kidney International, vol. 99, no. 1, pp. 86–101, 2021.
N. Bouteldja, B. M. Klinkhammer, R. D. Bülow, P. Droste, S. W. Otten, S. F. von Stillfried, J. Moellmann, S. M. Sheehan, R. Korstanje, S. Menzel et al., “Deep learning–based segmentation and quantification in experimental kidney histopathology,” Journal of the American Society of Nephrology, vol. 32, no. 1, pp. 52–68, 2021.
N. Altini, M. Rossini, S. Turkevi-Nagy, F. Pesce, P. Pontrelli, B. Prencipe, F. Berloco, S. Seshan, J.-B. Gibier, A. P. Dorado, G. Bueno, L. Peruzzi, M. Rossi, A. Eccher, F. Li, A. Koumpis, O. Beyan, J. Barratt, H. Q. Vo, C. Mohan, H. V. Nguyen, P. A. Cicalese, A. Ernst, L. Gesualdo, V. Bevilacqua, and J. U. Becker, “Performance and limitations of a supervised deep learning approach for the histopathological oxford classification of glomeruli with iga nephropathy,” Computer Methods and Programs in Biomedicine, p. 107814, 2023.
J. Silva, L. Souza, P. Chagas, R. Calumby, B. Souza, I. Pontes, A. Duarte, N. Pinheiro, W. Santos, and L. Oliveira, “Boundary-aware glomerulus segmentation: toward one-to-many stain generalization,” Computerized Medical Imaging and Graphics, vol. 100, p. 102104, 2022.
L. Souza, J. Silva, P. Chagas, A. Duarte, W. L.-d. Santos, and L. Oliveira, “Mouse-to-human transfer learning for glomerulus segmentation,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1–10, 2023.
T. de Bel, M. Hermsen, B. Smeets, L. Hilbrands, J. van der Laak, and G. Litjens, “Automatic segmentation of histopathological slides of renal tissue using deep learning,” in Medical Imaging 2018: Digital Pathology, vol. 10581. International Society for Optics and Photonics, 2018, p. 1058112.
J. N. Marsh, M. K. Matlock, S. Kudose, T.-C. Liu, T. S. Stappenbeck, J. P. Gaut, and S. J. Swamidass, “Deep learning global glomerulosclerosis in transplant kidney frozen sections,” IEEE transactions on medical imaging, vol. 37, no. 12, pp. 2718–2728, 2018.
M. Hermsen, T. de Bel, M. Den Boer, E. J. Steenbergen, J. Kers, S. Florquin, J. J. Roelofs, M. D. Stegall, M. P. Alexander, B. H. Smith et al., “Deep learning–based histopathologic assessment of kidney tissue,” Journal of the American Society of Nephrology, vol. 30, no. 10, pp. 1968–1979, 2019.
G. Bueno, M. M. Fernandez-Carrobles, L. Gonzalez-Lopez, and O. Deniz, “Glomerulosclerosis identification in whole slide images using semantic segmentation,” Computer methods and programs in biomedicine, vol. 184, pp. 1–10, 2020.
N. Altini, G. D. Cascarano, A. Brunetti, F. Marino, M. T. Rocchetti, S. Matino, U. Venere, M. Rossini, F. Pesce, L. Gesualdo et al., “Semantic segmentation framework for glomeruli detection and classification in kidney histological sections,” Electronics, vol. 9, no. 3, pp. 1–15, 2020.
R. C. Davis, X. Li, Y. Xu, Z. Wang, N. Souma, G. Sotolongo, J. Bell, M. Ellis, D. Howell, X. Shen et al., “Deep learning segmentation of glomeruli on kidney donor frozen sections,” medRxiv, pp. 1–27, 2021.
J. Gallego, Z. Swiderska-Chadaj, T. Markiewicz, M. Yamashita, M. A. Gabaldon, and A. Gertych, “A u-net based framework to quantify glomerulosclerosis in digitized pas and h&e stained human tissues,” Computerized Medical Imaging and Graphics, vol. 89, p. 101865, 2021.
L. Yu, M. Yin, R. Deng, Q. Liu, T. Yao, C. Cui, J. Guo, Y. Wang, Y. Wang, S. Zhao et al., “Glo-in-one-v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology,” Journal of Medical Imaging, vol. 12, no. 6, pp. 061 406–061 406, 2025.
X. Wang, J. Zhang, Y. Xu, Y. Huang, W. Ming, Y. Jiao, B. Liu, X. Fan, and J. Xu, “Glo-net: A dual task branch based neural network for multiclass glomeruli segmentation,” Computers in Biology and Medicine, vol. 186, p. 109670, 2025.
J. Min, D. Kang, and M. Cho, “Hypercorrelation squeeze for fewshot segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
X. Shi, D. Wei, Y. Zhang, D. Lu, M. Ning, J. Chen, K. Ma, and Y. Zheng, “Dense cross-query-and-support attention weighted mask aggregation for few-shot segmentation,” in European Conference on Computer Vision. Springer, 2022, pp. 151–168.
H. Chen, Y. Dong, Z. Lu, Y. Yu, and J. Han, “Pixel matching network for cross-domain few-shot segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 2024, pp. 978–987.
S. Hong, S. Cho, J. Nam, S. Lin, and S. Kim, “Cost aggregation with 4d convolutional swin transformer for few-shot segmentation,” in European Conference on Computer Vision. Springer, 2022, pp. 108–126.
Publicado
30/09/2025
Como Citar
SANTOS, Márcio dos; SOUZA, Luiz; FONTINELE, Jefferson; MENDONÇA, Marcelo; DUARTE, Angelo; DOS-SANTOS, Washington L. C.; OLIVEIRA, Luciano.
Can few-shot learning methods segment global-sclerotic glomeruli in WSI?. In: WORKSHOP ON DIGITAL AND COMPUTATIONAL PATHOLOGY - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 38. , 2025, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 364-368.
