{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:23:08Z","timestamp":1777044188704,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Science, Innovation and Universities","award":["PID2021-128009OB-C33"],"award-info":[{"award-number":["PID2021-128009OB-C33"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work presents the development of an Artificial Intelligence (AI)-based pipeline for patient-specific three-dimensional (3D) reconstruction from oncological magnetic resonance imaging (MRI), leveraging image-derived information to enhance the analysis process. These developments were carried out within the framework of Cella Medical Solutions, forming part of a broader initiative to improve and optimize the company\u2019s medical-image processing pipeline. The system integrates automatic MRI sequence classification using a ResNet-based architecture and segmentation of anatomical structures with a modular nnU-Net v2 framework. The classification stage achieved over 90% accuracy and showed improved segmentation performance over prior state-of-the-art pipelines, particularly for contrast-sensitive anatomies such as the hepatic vasculature and pancreas, where dedicated vascular networks showed Dice score differences of approximately 20\u201322%, and for musculoskeletal structures, where the model outperformed specialized networks in several elements. In terms of computational efficiency, the complete processing of a full MRI case, including sequence classification and segmentation, required approximately four minutes on the target hardware. The integration of sequence-aware information allows for a more comprehensive understanding of MRI signals, leading to more accurate delineations than approaches without such differentiation. From a clinical perspective, the proposed method has the potential to be integrated into surgical planning workflows. The segmentation outputs were converted into a patient-specific 3D model, which was subsequently integrated into Cella\u2019s surgical planner as a proof of concept. This process illustrates the transition from voxel-wise anatomical labels to a fully navigable 3D reconstruction, representing a step toward more robust and personalized AI-driven medical-image analysis workflows that leverage sequence-aware information for enhanced clinical utility.<\/jats:p>","DOI":"10.3390\/s26010323","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T10:53:50Z","timestamp":1767610430000},"page":"323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Surgical Planning with AI-Driven Segmentation and Classification of Oncological MRI Scans"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6488-9478","authenticated-orcid":false,"given":"Alejandro","family":"Martinez Guillermo","sequence":"first","affiliation":[{"name":"Escuela Tecnica Superior de Ingenieria Industrial, Campus Muralla del Mar, Universidad Politecnica de Cartagena Member of European University of Technology EUT+, C\/Doctor Fleming, s\/n, 30202 Cartagena, Spain"},{"name":"Cella Medical Solutions, 30100 Murcia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7798-3928","authenticated-orcid":false,"given":"Juan Francisco","family":"Zapata P\u00e9rez","sequence":"additional","affiliation":[{"name":"Escuela Tecnica Superior de Ingenieria Industrial, Campus Muralla del Mar, Universidad Politecnica de Cartagena Member of European University of Technology EUT+, C\/Doctor Fleming, s\/n, 30202 Cartagena, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5193-7800","authenticated-orcid":false,"given":"Juan","family":"Martinez-Alajarin","sequence":"additional","affiliation":[{"name":"Escuela Tecnica Superior de Ingenieria Industrial, Campus Muralla del Mar, Universidad Politecnica de Cartagena Member of European University of Technology EUT+, C\/Doctor Fleming, s\/n, 30202 Cartagena, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alicia","family":"Ar\u00e9valo Garc\u00eda","sequence":"additional","affiliation":[{"name":"Cella Medical Solutions, 30100 Murcia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,4]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. 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