{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T21:53:52Z","timestamp":1788386032933,"version":"build-2803163510"},"reference-count":140,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Ministry of National Resources","award":["2.3.2-15-2016-00030"],"award-info":[{"award-number":["2.3.2-15-2016-00030"]}]},{"name":"the Ministry of National Resources","award":["2.3.2-15-2016-00034"],"award-info":[{"award-number":["2.3.2-15-2016-00034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) involves the accumulation of amyloid-\u03b2 (A\u03b2) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of A\u03b2 deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly supervised learning offers a promising alternative by leveraging coarse or indirect labels to reduce the annotation burden. We evaluated a weakly supervised approach to segment and analyze thioflavin-S-positive parenchymal amyloid pathology in AD and age-matched brains. Our pipeline integrates three key components, each designed to operate under weak supervision. First, robust preprocessing (including retrospective multi-image illumination correction and gradient-based background estimation) was applied to enhance image fidelity and support training, as models rely more on image features. Second, class activation maps (CAMs), generated by a compact deep classifier SqueezeNet, were used to identify, and coarsely localize amyloid-rich parenchymal regions from patch-wise image labels, serving as spatial priors for subsequent refinement without requiring dense pixel-level annotations. Third, a patch-based convolutional neural network, U-Net, was trained on synthetic data generated from micrographs based on CAM-derived pseudo-labels via an extensive object-level augmentation strategy, enabling refined whole-image semantic segmentation and generalization across diverse spatial configurations. To ensure robustness and unbiased evaluation, we assessed the segmentation performance of the entire framework using patient-wise group k-fold cross-validation, explicitly modeling generalization across unseen individuals, critical in clinical scenarios. Despite relying on weak labels, the integrated pipeline achieved strong segmentation performance with an average Dice similarity coefficient (\u22480.763) and Jaccard index (\u22480.639), widely accepted metrics for assessing segmentation quality in medical image analysis. The resulting segmentations were also visually coherent, demonstrating that weakly supervised segmentation is a viable alternative in histopathology, where acquiring dense annotations is prohibitively labor-intensive and time-consuming. Subsequent morphometric analyses on automatically segmented A\u03b2 deposits revealed size-, structural complexity-, and global geometry-related differences across brain regions and cognitive status. These findings confirm that deposit architecture exhibits region-specific patterns and reflects underlying neurodegenerative processes, thereby highlighting the biological relevance and practical applicability of the proposed image-processing pipeline for morphometric analysis.<\/jats:p>","DOI":"10.3390\/ijms26157134","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T10:28:09Z","timestamp":1753352889000},"page":"7134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated Segmentation and Morphometric Analysis of Thioflavin-S-Stained Amyloid Deposits in Alzheimer\u2019s Disease Brains and Age-Matched Controls Using Weakly Supervised Deep Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"G\u00e1bor","family":"Barcz\u00e1nfalvi","sequence":"first","affiliation":[{"name":"Department of Cell Biology and Molecular Medicine, University of Szeged, 6720 Szeged, Hungary"},{"name":"Department of Medical Physics and Informatics, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8900-6641","authenticated-orcid":false,"given":"Tibor","family":"Ny\u00e1ri","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Informatics, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7648-764X","authenticated-orcid":false,"given":"J\u00f3zsef","family":"Tolnai","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Informatics, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L\u00e1szl\u00f3","family":"Tiszlavicz","sequence":"additional","affiliation":[{"name":"Department of Pathology, University of Szeged, 6725 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9295-2460","authenticated-orcid":false,"given":"Bal\u00e1zs","family":"Guly\u00e1s","sequence":"additional","affiliation":[{"name":"Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden"},{"name":"Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6864-4150","authenticated-orcid":false,"given":"Karoly","family":"Gulya","sequence":"additional","affiliation":[{"name":"Department of Cell Biology and Molecular Medicine, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/BF00308809","article-title":"Neuropathological stageing of Alzheimer-related changes","volume":"82","author":"Braak","year":"1991","journal-title":"Acta Neuropathol."},{"key":"ref_2","first-page":"396","article-title":"Is sporadic Alzheimer\u2019s disease a developmental disorder?","volume":"143","author":"Arendt","year":"2017","journal-title":"J. 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