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 similar. However, the large variability of IAs and their parent vasculature configurations often leads to failure or non-intuitive isolation. Manual corrections are thus required, but suffer from poor reproducibility. In this paper, we aim to increase the accuracy, robustness and reproducibility of IA isolation through two stage deep learning based segmentation of vascular surface. The surface was represented by local patches in form of point clouds, which were fed into first stage multilayer neural network (MNN) to obtain descriptors invariant to point ordering, rotation and scale. Binary classifier as second stage MNN was used to isolate surface belonging to the IA. Method validation was based on 57 DSA, 28 CTA and 5 MRA images, where cross-validation showed high segmentation sensitivity of 0.985, a substantial improvement over 0.830 obtained for the state-of-the-art method on the same datasets. Visual analysis of IA isolation and its high accuracy and reliability consistent across CTA and DSA scans confirmed the clinical applicability of proposed method.
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…
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…
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…
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 …
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 …
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…
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）
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；School of Aeronautics and Astronautics, Dalian University of Technology（大连理工大学航空航天学院）；Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院介入神经放射科）；Ultrasonic Department, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital（河北医科大学第四医院暨河北省肿瘤医院超声科）；School of Computer Science and Information Security, Guilin University of Electronic Technology（桂林电子科技大学计算机科学与信息安全学院）；Department of Radiology, The Netherlands Cancer Institute（荷兰癌症研究所放射科）；Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical（伊拉斯姆斯大学医学中心放射与核医学科）
Capital Medical University（首都医科大学）；China National Clinical Research Center for Neurological Diseases（国家神经系统疾病临床医学研究中心）；Tencent（腾讯）；Shandong Jining Medical College（山东济宁医学院）
Background: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology based on non-contrast CT (NCCT) scans and investigate whether clinicians can benefit from it in a diagnostic setting. Materials and Methods: The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. We tested the model on two independent datasets (TT200 and SD 98) collected after April 2018. The model's diagnostic performance was compared with clinicians's performance. We …
Vessel tracing by modeling vascular structures in 3D medical images with centerlines and radii can provide useful information for vascular health. Existing algorithms have been developed but there are certain persistent problems such as incomplete or inaccurate vessel tracing, especially in complicated vascular beds like the intracranial arteries. We propose here a deep learning based open curve active contour model (DOST) to trace vessels in 3D images. Initial curves were proposed from a centerline segmentation neural network. Then data-driven machine knowledge was used to predict the stretch…
University of Twente（特文特大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究所）
Personalised 3D vascular models are valuable for diagnosis, prognosis and treatment planning in patients with cardiovascular disease. Traditionally, such models have been constructed with explicit representations such as meshes and voxel masks, or implicit representations such as radial basis functions or atomic (tubular) shapes. Here, we propose to represent surfaces by the zero level set of their signed distance function (SDF) in a differentiable implicit neural representation (INR). This allows us to model complex vascular structures with a representation that is implicit, continuous, light…
Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
Brigham and Women’s Hospital Harvard Medical School（哈佛医学院布莱尔妇女医院）；Khoury College of Computer Sciences Northeastern University（东北大学计算机科学学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；Dana-Farber Cancer Institute Harvard Medical School（哈佛医学院丹娜-法伯癌症研究院）
Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation
University of Twente（特文特大学）；Amsterdam UMC location Vrije Universiteit Amsterdam（阿姆斯特丹大学医学中心（阿姆斯特丹自由大学医学中心））；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究院）
ArteryX: A Reliable End-to-End Toolbox for Standardized Intracranial Artery Feature Extraction from 3D TOF-MRA
Department of Neurology, University of Rochester（罗切斯特大学神经科部）；Department of Physics, University of Rochester（罗切斯特大学物理系）；Department of Imaging Sciences, University of Rochester（罗切斯特大学影像科学系）；Department of Electrical & Computer Engineering, University of Rochester（罗切斯特大学电气与计算机工程系）；Department of Biomedical Engineering, University of Rochester（罗切斯特大学生物医学工程系）
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；AMEOS Hospital Bernburg（AMEOS 伯恩堡医院）
Background and objective: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. On selected benchmarking datasets, it outperformed state-of-the-art methods within classification and segmentation tasks. Especially, the medical domain provides a large amount of complex 3D surface models that may benefit from processing with MeshCNN. However, several limitations prevent outstanding performances of MeshCNN on highly diverse medical surface models. Within this work, we propose MedMeshCNN as an expansion for comple…
