Accurate identification and quantification of unruptured intracranial aneurysms (UIAs) is crucial for the risk assessment and treatment of this cerebrovascular disorder. Current 2D manual assessment on 3D magnetic resonance angiography (MRA) is suboptimal and time-consuming. In addition, one major issue in medical image segmentation is the need for large well-annotated data, which can be expensive to obtain. Techniques that mitigate this requirement, such as weakly supervised learning with coarse labels are highly desirable. In the paper, we propose FocalSegNet, a novel 3D focal modulation UNet, to detect an aneurysm and offer an initial, coarse segmentation of it from time-of-flight MRA image patches, which is further refined with a dense conditional random field (CRF) post-processing layer to produce a final segmentation map. We trained and evaluated our model on a public dataset, and in terms of UIA detection, our model showed a low false-positive rate of 0.21 and a high sensitivity of 0.80. For voxel-wise aneurysm segmentation, we achieved a Dice score of 0.68 and a 95% Hausdorff distance of ~0.95 mm, demonstrating its strong performance. We evaluated our algorithms against the state-of-the-art 3D Residual-UNet and Swin-UNETR, and illustrated the superior performance of our proposed FocalSegNet, highlighting the advantages of employing focal modulation for this task.
Intracranial aneurysms are common nowadays and how to detect them intelligently is of great significance in digital health. While most existing deep learning research focused on medical images in a supervised way, we introduce an unsupervised method for the detection of intracranial aneurysms based on 3D point cloud data. In particular, our method consists of two stages: unsupervised pre-training and downstream tasks. As for the former, the main idea is to pair each point cloud with its jittered counterpart and maximise their correspondence. Then we design a dual-branch contrastive network wit…
Union Strong Technology Beijing Co.（北京联强科技公司）；The University of Virginia（弗吉尼亚大学）；Beijing Tiantan hospital（北京天坛医院）
Intracranial aneurysm (IA) is a life-threatening blood spot in human's brain if it ruptures and causes cerebral hemorrhage. It is challenging to detect whether an IA has ruptured from medical images. In this paper, we propose a novel graph based neural network named GraphNet to detect IA rupture from 3D surface data. GraphNet is based on graph convolution network (GCN) and is designed for graph-level classification and node-level segmentation. The network uses GCN blocks to extract surface local features and pools to global features. 1250 patient data including 385 ruptured and 865 unruptured …
The University of Tokyo（东京大学）；South China University of Technology（华南理工大学）
Medicine is an important application area for deep learning models. Research in this field is a combination of medical expertise and data science knowledge. In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation models available. Our dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction. We provide a …
The exact shape of intracranial aneurysms is critical in medical diagnosis and surgical planning. While voxel-based deep learning frameworks have been proposed for this segmentation task, their performance remains limited. In this study, we offer a two-step surface-based deep learning pipeline that achieves significantly higher performance. Our proposed model takes a surface model of entire principal brain arteries containing aneurysms as input and returns aneurysms surfaces as output. A user first generates a surface model by manually specifying multiple thresholds for time-of-flight magnetic…
SynVA: A Modular Toolkit for Vessel Generation and Aneurysm Editing
Visual Computing and Artificial Intelligence, Kiel University, Germany（视觉计算与人工智能研究所，基尔大学，德国）；Institute for Medical Informatics and Statistics, Kiel University, Germany（医学信息学与统计研究所，基尔大学，德国）；Clinic for Neuroradiology, Medical Faculty, Magdeburg University, Germany（神经放射科，马格德堡大学医学学院，德国）；Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Germany（放射学与神经放射学部门，石勒苏益格-荷尔斯泰因大学医院，德国）；Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland（数学与计算机科学学院，亚当·密茨凯维奇大学，波兰）
Weakly Supervised Intracranial Aneurysm Detection and Segmentation in MR angiography via Multi-task UNet with Vesselness Prior
Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada（计算机科学与软件工程系，康科迪亚大学）；NeuroRx Research, Montréal, Canada（NeuroRx研究公司）；Department of Electrical and Computer Engineering, Concordia University, Montréal, Canada（电气与计算机工程系，康科迪亚大学）
The growth of unruptured intracranial aneurysms (UIAs) is a predictor of rupture. Therefore, for further imaging surveillance and treatment planning, it is important to be able to predict if an UIA is likely to grow based on an initial baseline Time-of-Flight MRA (TOF-MRA). It is known that the size and shape of UIAs are predictors of aneurysm growth and/or rupture. We perform a feasibility study of using a mesh convolutional neural network for future UIA growth prediction from baseline TOF-MRAs. We include 151 TOF-MRAs, with 169 UIAs where 49 UIAs were classified as growing and 120 as stable,…
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院高级医学计算与分析中心）；Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院戈登医学影像中心）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院放射科）
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network. Methods: In this retrospective study, we evaluated 1,216 patients from two separate institutions who underwent CT for the presence of saccular IA>=2.5 mm. A two-step model was implemented: a 3D region proposal network for initial aneurysm detection and 3D DenseNetsfor false-pos…
Intracranial aneurysms (IAs) are generally asymptomatic and thus often discovered incidentally on angiographic scans like 3D DSA, CTA and MRA. Skilled radiologists achieved a sensitivity of 88% by means of visual detection, which seems inadequate considering that prevalence of IAs in general population is 3-5%. Deep learning models trained and executed on angiographic scans seem best-suited for IA detection, however, reported performances across different modalities is currently insufficient for clinical application. This paper presents a novel modality agnostic method for detection of IAs. Fi…
Predicting rupture risk and deciding on optimal treatment plan for intracranial aneurysms (IAs) is possible by quantification of their size and shape. For this purpose the IA has to be isolated from 3D angiogram. State-of-the-art methods perform IA isolation by encoding neurosurgeon's intuition about former non-dilated vessel anatomy through principled approaches like fitting a cutting plane to vasculature surface, using Gaussian curvature and vessel centerline distance constraints, by deformable contours or graph cuts guided by the curvature or restricted by Voronoi surface decomposition and …
Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning
Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Brigham and Women’s Hospital（布列根和妇女医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Washing University in St. Louis（圣路易斯华盛顿大学）；First Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第一医院）
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
Data and Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany（数据与知识工程组，计算机科学系，奥托·冯·格里克大学马格德堡分校）；Genomics Research Centre, Human Technopole, Milan, Italy（基因组研究中心，人类技术pole，米兰，意大利）；Laboratory of Fluid Dynamics and Technical Flows, Otto von Guericke University Magdeburg, Germany（流体动力学与技术流动实验室，奥托·冯·格里克大学马格德堡分校）；Department of Biomedical Magnetic Resonance, Faculty of Natural Sciences, Otto von Guericke University Magdeburg, Germany（生物医学磁共振部门，自然科学系，奥托·冯·格里克大学马格德堡分校）；MedDigit, Department of Neurology, Medical Faculty, University Hospital, Magdeburg, Germany（MedDigit，神经病学部门，医学学院，大学医院，马格德堡，德国）；German Centre for Neurodegenerative Disease, Magdeburg, Germany（德国神经退行性疾病中心，马格德堡，德国）；Centre for Behavioural Brain Sciences, Magdeburg, Germany（行为脑科学中心，马格德堡，德国）；School of Mathematics & Statistics, UNSW Sydney and UNSW Data Science Hub（数学与统计学学院，悉尼大学和UNSW数据科学中心）
本文提出 PULASki，在 Probabilistic UNet 中引入基于统计距离的损失以建模专家标注差异，并在颅内血管和 MS 病灶任务上显著优于四种基线。
