Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,...最新文献

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My Model Is Better Than Yours! Statistically-aware Ranking for Fair Benchmarking of AI Models. 我的模型比你的好！人工智能模型公平基准的统计感知排名。

Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,... Pub Date : 2026-01-01 Epub Date: 2026-04-01 DOI: 10.1007/978-3-032-16365-3_1

Spyridon Bakas, Siddhesh Thakur, Ujjwal Baid, Akis Linardos, Sarthak Pati, Jimit Doshi, Russell T Shinohara

{"title":"My Model Is Better Than Yours! Statistically-aware Ranking for Fair Benchmarking of AI Models.","authors":"Spyridon Bakas, Siddhesh Thakur, Ujjwal Baid, Akis Linardos, Sarthak Pati, Jimit Doshi, Russell T Shinohara","doi":"10.1007/978-3-032-16365-3_1","DOIUrl":"10.1007/978-3-032-16365-3_1","url":null,"abstract":"The proliferation of artificial intelligence (AI) in healthcare has triggered the need of fair benchmarking. This, in turn, has inspired the rise of computational challenges, where participants worldwide submit models under standardized evaluation protocols. However, ranking AI models -particularly when declaring winners- raises questions about their difference from the next best (but lower-ranked) model. Here, we present a statistically-aware ranking framework, PermRanker , designed to be agnostic to computational workloads (e.g., segmentation, classification, registration) and underlying data types (e.g., 2D pathology images, 3D MRI scans, or even non-imaging data). PermRanker provides fair and informative benchmarking of AI models, based on two stages: (i) a ranking score, based on case-wise cumulative rankings aggregated across multiple metrics and testing cases, and (ii) a rigorous statistical significance analysis via pairwise permutation testing across the ranked order of the AI models. This framework has served as the official ranking mechanism for over 33 international challenges between 2017 and 2025, including the BraTS, FeTS, and ISLES challenges. While mainly applied in biomedical AI challenges, PermRanker aims to address the unmet need of fair and informative benchmarking of AI models beyond this scope, tackle actual real-world conditions, and hence contribute in streamlining clinical translation of AI models.","PeriodicalId":521404,"journal":{"name":"Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,...","volume":"16376 ","pages":"3-13"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13526430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148869581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-guided Subtyping and Lesion-Wise Model Ensemble. 基于放射学引导和基于病灶的模型集成的不同脑肿瘤的自适应分割管道。

Daniel Capellán-Martín, Abhijeet Parida, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, María J Ledesma-Carbayo, Marius George Linguraru

{"title":"Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-guided Subtyping and Lesion-Wise Model Ensemble.","authors":"Daniel Capellán-Martín, Abhijeet Parida, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, María J Ledesma-Carbayo, Marius George Linguraru","doi":"10.1007/978-3-032-16365-3_41","DOIUrl":"10.1007/978-3-032-16365-3_41","url":null,"abstract":"Robust and generalizable segmentation of brain tumors on multi-parametric magnetic resonance imaging (MRI) remains difficult because tumor types differ widely. The BraTS 2025 Lighthouse Challenge benchmarks segmentation methods on diverse high-quality datasets of adult and pediatric tumors: multi-consortium international pediatric brain tumor segmentation (PED), preoperative meningioma tumor segmentation (MEN), meningioma radiotherapy segmentation (MEN-RT), and segmentation of pre- and post-treatment brain metastases (MET). We present a flexible, modular, and adaptable pipeline that improves segmentation performance by selecting and combining state-of-the-art models and applying tumor- and lesion-specific processing before and after training. Radiomic features extracted from MRI help detect tumor subtype, ensuring a more balanced training. Custom lesion-level performance metrics determine the influence of each model in the ensemble and optimize post-processing that further refines the predictions, enabling the workflow to tailor every step to each case. On the BraTS testing sets, our pipeline achieved performance comparable to top-ranked algorithms across multiple challenges. These findings confirm that custom lesion-aware processing and model selection yield robust segmentations yet without locking the method to a specific network architecture. Our method has the potential for quantitative tumor measurement in clinical practice, supporting diagnosis and prognosis.","PeriodicalId":521404,"journal":{"name":"Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,...","volume":"16376 ","pages":"456-467"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13148016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Pre-trained Adult Glioma Segmentation Models using only Post-processing Techniques. 仅使用后处理技术改进预训练的成人胶质瘤分割模型。

Abhijeet Parida, Daniel Capellán-Martín, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, María J Ledesma-Carbayo, Marius George Linguraru

{"title":"Improving Pre-trained Adult Glioma Segmentation Models using only Post-processing Techniques.","authors":"Abhijeet Parida, Daniel Capellán-Martín, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, María J Ledesma-Carbayo, Marius George Linguraru","doi":"10.1007/978-3-032-16365-3_22","DOIUrl":"10.1007/978-3-032-16365-3_22","url":null,"abstract":"Gliomas are the most common malignant brain tumors in adults and are among the most lethal. Despite aggressive treatment, the median survival rate is less than 15 months. Accurate multiparametric MRI (mpMRI) tumor segmentation is critical for surgical planning, radiotherapy, and disease monitoring. While deep learning models have improved the accuracy of automated segmentation, large-scale pre-trained models generalize poorly and often underperform, producing systematic errors such as false positives, label swaps, and slice discontinuities in slices. These limitations are further compounded by unequal access to GPU resources and the growing environmental cost of large-scale model training. In this work, we propose adaptive post-processing techniques to refine the quality of glioma segmentations produced by large-scale pretrained models developed for various types of tumors. We demonstrated the techniques in multiple BraTS 2025 segmentation challenge tasks, with the ranking metric improving by 14.9 % for the sub-Saharan Africa challenge and 0.9% for the adult glioma challenge. This approach promotes a shift in brain tumor segmentation research from increasingly complex model architectures to efficient, clinically aligned post-processing strategies that are precise, computationally fair, and sustainable.","PeriodicalId":521404,"journal":{"name":"Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,...","volume":"16376 ","pages":"237-247"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13178704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147966756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0