{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:16:51Z","timestamp":1760059011206,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a crucial task for diagnosing and treating liver diseases. However, these models often face challenges in adapting to diverse clinical data sources as differences in dataset volume, resolution, and origin impact generalization and performance. This study introduces a Private Data Incrementalization, a data-centric approach to enhance the adaptability of Artificial Neural Networks by progressively exposing them to varied clinical data. As the target of this study is not to propose a new image segmentation model, the existing medical imaging segmentation models\u2014including U-Net, ResUNet++, Fully Convolutional Network, and a modified algorithm based on the Conditional Bernoulli Diffusion Model\u2014are used. The study evaluates these four models using a curated private dataset of computed tomography scans from Coimbra University Hospital, supplemented by two public datasets, 3D-IRCADb01 and CHAOS. The Private Data Incrementalization method systematically increases the volume and diversity of training data, simulating real-world conditions where models must handle varied imaging contexts. Pre-processing and post-processing stages, incremental training, and performance evaluations reveal that structured exposure to diverse datasets improves segmentation performance, with ResUNet++ achieving the highest accuracy (0.9972) and Dice Similarity Coefficient (0.9449), and the best Average Symmetric Surface Distance (0.0053 mm), demonstrating the importance of dataset diversity and volume for segmentation models\u2019 robustness and generalization. Private Data Incrementalization thus offers a scalable strategy for building resilient segmentation models, ultimately benefiting clinical workflows, patient care, and healthcare resource management by addressing the variability inherent in clinical imaging data.<\/jats:p>","DOI":"10.3390\/bioengineering12050530","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T09:58:27Z","timestamp":1747303107000},"page":"530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7352-5495","authenticated-orcid":false,"given":"Stephanie","family":"Batista","sequence":"first","affiliation":[{"name":"Polytechnic University of Coimbra, Rua da Miseric\u00f3rdia, Lagar dos Corti\u00e7os, S. Martinho do Bispo, 3045-093 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6800-8653","authenticated-orcid":false,"given":"Miguel","family":"Couceiro","sequence":"additional","affiliation":[{"name":"Polytechnic University of Coimbra, Rua da Miseric\u00f3rdia, Lagar dos Corti\u00e7os, S. Martinho do Bispo, 3045-093 Coimbra, Portugal"},{"name":"Institute of Applied Research (i2A), 3045-093 Coimbra, Portugal"},{"name":"Laboratory for High Performance Computing (LaCED), 3030-199 Coimbra, Portugal"},{"name":"Laboratory of Instrumentation and Experimental Particle Physics (LIP-Coimbra), Rua Larga da Universidade de Coimbra, 3004-516 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4438-4176","authenticated-orcid":false,"given":"Ricardo","family":"Filipe","sequence":"additional","affiliation":[{"name":"Altice Labs, S.A, 3810-106 Aveiro, Portugal"}]},{"given":"Paulo","family":"Rachinhas","sequence":"additional","affiliation":[{"name":"Coimbra Hospital and University Center, 3004-561 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4402-6918","authenticated-orcid":false,"given":"Jorge","family":"Isidoro","sequence":"additional","affiliation":[{"name":"Coimbra Hospital and University Center, 3004-561 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2334-7280","authenticated-orcid":false,"given":"In\u00eas","family":"Domingues","sequence":"additional","affiliation":[{"name":"Polytechnic University of Coimbra, Rua da Miseric\u00f3rdia, Lagar dos Corti\u00e7os, S. Martinho do Bispo, 3045-093 Coimbra, Portugal"},{"name":"Medical Physics, Radiobiology and Radiological Protection Group, Research Centre of the Portuguese Institute of Oncology of Porto (CI-IPOP), 4200-072 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ni\u00f1o, S.B., Bernardino, J., and Domingues, I. (2024). Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective. Sensors, 24.","DOI":"10.20944\/preprints202402.0464.v1"},{"key":"ref_2","unstructured":"Batista, S., and Domingues, I. (2024, January 25). Adoption of Artificial Neural Network-based Methods for Medical Image Segmentation. 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