arXiv Machine Learning
Oct 15

Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR

The paper introduces Contrastive Uncertainty Learning (CUL), a supervised framework that leverages voxel‑wise predictive uncertainty to guide representation learning for multi‑structure segmentation in lower‑pelvic MRI. Using Monte Carlo Dropout, CUL identifies uncertain voxels and shapes the embedding space so that low‑uncertainty, correctly predicted features become stable while high‑uncertainty or misclassified features are repelled, preventing feature collapse at challenging boundaries. Evaluated on 589 prostate MRI scans with a ViT‑based UperNet decoder, CUL achieves comparable Dice scores to a baseline while significantly reducing surface distance for most structures, and ablation studies confirm the necessity of both uncertainty‑guided sampling and the contrastive objective.

By Bilal Sidiqi, Yipei Wang, Wen Yan, Shaheer U. Saeed, Daniel C. Alexander, Yipeng Hu
arXiv Computer Vision
Aug 27

Deep Learning Segmentation of Diffusion-Weighted MRI Acute Ischaemic Stroke: A Pragmatic Evaluation Across Three Datasets

The study evaluated a pragmatic deep‑learning approach for segmenting acute ischemic stroke lesions on diffusion‑weighted MRI. Using a self‑configured nnU‑Net trained on 1,744 cases and tested on 436, the baseline model achieved a median Dice similarity coefficient of 0.84, outperforming the DeepISLES ensemble, especially for smaller infarcts. The approach required minimal preprocessing and fast inference, suggesting it could streamline clinical stroke imaging workflows.

By Atle Bj{\o}rnerud, Till Schellhorn, Thor H. Skatt{\o}r, Terje Nome, Jon Andr\'e Ottesen, Anne Hege Aamodt, Bradley J MacIntosh
arXiv Computer Vision
1d ago

Conditional Diffusion-Based Generation of T2-Weighted Prostate MRI for Clinically Significant Prostate Cancer Classification

The study presents a conditional diffusion-based generative model that augments T2-weighted prostate MRI data with clinically significant prostate cancer (csPCa) and transition zone (TZ) lesions. By extending MAISI‑v2 with csPCa class, zonal, and lesion‑mask conditioning and fine‑tuning on 1,303 patients, the model achieves a substantial reduction in Fréchet inception distance (FID) from 13.28 to 2.04. Synthetic augmentation improves csPCa classification AUC across five simulated training scenarios, though the benefit does not significantly transfer to a cross‑institutional dataset.

By Xavier Rafael-Palou (Quantitative Imaging Biomarkers in Medicine), Alejandro Vergara-Richart (Quantitative Imaging Biomarkers in Medicine, Universitat Polit\`ecnica de Val\`encia, Val\`encia, Spain), Ignacio Iborra Roncales (Quantitative Imaging Biomarkers in Medicine), Carmen Prieto-de-la-Lastra (Quantitative Imaging Biomarkers in Medicine), Andreu Antolin-Redondo (Department of Radiology, Institut de Diagn\`ostic per la Imatge, Department of Surgery, Universitat Aut\`onoma de Barcelona, Bellaterra, Spain), Juan Morote (Department of Surgery, Universitat Aut\`onoma de Barcelona, Bellaterra, Spain), Ana Jimenez-Pastor (Quantitative Imaging Biomarkers in Medicine)
arXiv AI
Jun 3

Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis

arXiv:2606. 03566v1 Announce Type: cross Abstract: Background: The lateral ventricle choroid plexus (LVCP) is gaining recognition as a key imaging biomarker for multiple sclerosis (MS) related to physical disability and neuroinflammation.

By Po-Jui Lu, Alessandro Cagol, Mario Ocampo-Pineda, Federico Spagnolo, Marina Mastantuono, Andreea-Alexandra Aldea, Jannis M\"uller, \"Ozg\"ur Yaldizli, Matthias Weigel, Lester Melie-Garcia, Roberta Magliozzi, Maria Pia Sormani, Ludwig Kappos, Jens Kuhle, Cristina Granziera