Intracerebral and Subarachnoid Hemorrhage Research

Últimos artículos

• CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detection

  Lucas He, Hanyuan Zhang, Krinos Li, Adama Fatima Saccoh, Silvia Ingala, Rafael Rehwald, Marleen de Bruijne, Frederik Barkhof, Rhodri Davies, Carole H. Sudre · 7 de julio de 2026

  Cerebral microbleeds (CMBs) are MRI markers of small vessel disease and the microbleed component of amyloid related imaging abnormalities (ARIA-H), but their small size, sparsity, and similarity to vessels, calcification-like foci, and artefacts make automated detection difficult. We propose CenSynC…

• HemExp: Clinically-Guided Latent Diffusion for Modeling Hematoma Expansion

  Orhun Utku Aydin, Satoru Tanioka, Tzu I Chuang, Alexander Koch, Dimitrios Rallios, Marie Gultom, Begum Tahhan, Fujimaro Ishida, Dietmar Frey, Adam Hilbert · 16 de junio de 2026

  Hematoma expansion (HE) after spontaneous intracerebral hemorrhage (ICH) is a major determinant of acute triage and treatment decisions in neurosurgical care. However, most existing methods provide either a binary expansion risk or a single follow-up volume, limiting uncertainty-aware decisions. We …

• AI-Powered Intracranial Hemorrhage Detection: A Co-Scale Convolutional Attention Model with Uncertainty-Based Fuzzy Integral Operator and Feature Screening

  Mehdi Hosseini Chagahi, Niloufar Delfan, Behzad Moshiri, Md. Jalil Piran, Jaber Hatam Parikhan · 10 de febrero de 2026

  Intracranial hemorrhage (ICH) refers to the leakage or accumulation of blood within the skull, which occurs due to the rupture of blood vessels in or around the brain. If this condition is not diagnosed in a timely manner and appropriately treated, it can lead to serious complications such as decrea…

• CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking

  Amirreza Parvahan, Mohammad Hoseyni, Javad Khoramdel, Amirhossein Nikoofard · 7 de enero de 2026

  Automated analysis of volumetric medical imaging on edge devices is severely constrained by the high memory and computational demands of 3D Convolutional Neural Networks (CNNs). This paper develops a lightweight computer vision framework that reconciles the efficiency of 2D detection with the necess…

• Detection and Localization of Subdural Hematoma Using Deep Learning on Computed Tomography

  Vasiliki Stoumpou, Rohan Kumar, Bernard Burman, Diego Ojeda, Tapan Mehta, Dimitris Bertsimas · 11 de diciembre de 2025

  Background. Subdural hematoma (SDH) is a common neurosurgical emergency, with increasing incidence in aging populations. Rapid and accurate identification is essential to guide timely intervention, yet existing automated tools focus primarily on detection and provide limited interpretability or spat…