We propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans. The CNN plays the role of a slice-wise feature extractor while the LSTM is responsible for linking the features across slices. The whole architecture is trained end-to-end with input being an RGB-like image formed by stacking 3 different viewing windows of a single slice. We validate the method on the recent RSNA Intracranial Hemorrhage Detection challenge and on the CQ500 dataset. For the RSNA challenge, our best single model achieves a weighted log loss of 0.0522 on the leaderboard, which is comparable to the top 3% performances, almost all of which make use of ensemble learning. Importantly, our method generalizes very well: the model trained on the RSNA dataset significantly outperforms the 2D model, which does not take into account the relationship between slices, on CQ500. Our codes and models is publicly avaiable at https://github.com/VinBDI-MedicalImagingTeam/midl2020-cnnlstm-ich.
University of Bucharest（布加勒斯特大学）；Romanian Young Academy（罗马尼亚青年科学院）；``Carol Davila'' University of Medicine and Pharmacy（“卡罗尔·达维拉”医药大学）
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2x, allowing ourselves to train the model faster. …
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland School of Medicine（马里兰大学医学院）
Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing strong supervision on what constitutes positive and negative samples. This constitutes a major obstacle for the development of DL models in radiology--in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)--where labels must come from manual annotations by expert radiologists at the image or slice-level. The…
Florida Atlantic University（佛罗里达大西洋大学）；University of Technology（理工大学）；Babylon Health Directorate（巴比伦卫生局）
Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each sl…
Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping
Image Processing Center, Beihang University（北京航空航天大学图像处理中心）；School of Mechanical Engineering and Automation, Beihang University（北京航空航天大学机械工程与自动化学院）；Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院神经内科，同济医院）
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
Vingroup Big Data Institute（Vingroup大数据研究院）；VinBigData JSC（VinBigData股份公司）；VinUni-Illinois Smart Health Center, College of Engineering & Computer Science（VinUni-伊利诺伊智慧健康中心，工程与计算机科学学院）
St. Michael’s Hospital（圣迈克尔医院）；University of Toronto（多伦多大学）
Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demo…
Vision Transformer for Intracranial Hemorrhage Classification in CT Scans Using an Entropy-Aware Fuzzy Integral Strategy for Adaptive Scan-Level Decision Fusion
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（塔里斯坦大学电气与计算机工程学院）；Department of Computer Science and Engineering, Sejong University（世宗大学计算机科学与工程系）
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 …
Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform
Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine（计算放射学与人工智能单元，物理与计算放射学部门，放射科与核医学诊所）；Oslo University Hospital（奥斯陆大学医院）；Department of Physics（物理部门）；University of Oslo（奥斯陆大学）；Department of Medical Biophysics（医学生物物理学部门）；University of Toronto（多伦多大学）
University of California Berkeley（加州大学伯克利分校）；University of California San Francisco School of Medicine（加州大学旧金山分校医学院）
This paper studies the problem of detecting and segmenting acute intracranial hemorrhage on head computed tomography (CT) scans. We propose to solve both tasks as a semantic segmentation problem using a patch-based fully convolutional network (PatchFCN). This formulation allows us to accurately localize hemorrhages while bypassing the complexity of object detection. Our system demonstrates competitive performance with a human expert and the state-of-the-art on classification tasks (0.976, 0.966 AUC of ROC on retrospective and prospective test sets) and on segmentation tasks (0.785 pixel AP, 0.…
We present an effective method for Intracranial Hemorrhage Detection (IHD) which exceeds the performance of the winner solution in RSNA-IHD competition (2019). Meanwhile, our model only takes quarter parameters and ten percent FLOPs compared to the winner's solution. The IHD task needs to predict the hemorrhage category of each slice for the input brain CT. We review the top-5 solutions for the IHD competition held by the Radiological Society of North America(RSNA) in 2019. Nearly all the top solutions rely on 2D convolutional networks and sequential models (Bidirectional GRU or LSTM) to extra…
Intracranial hemorrhage is a life-threatening disease, which requires fast medical intervention. Owing to the duration of data annotation, head CT images are usually available only with slice-level labeling. This paper presents a weakly supervised method of precise hemorrhage localization in axial slices using only position-free labels, which is based on multiple instance learning. An algorithm is introduced that generates hemorrhage likelihood maps and finds the coordinates of bleeding. The Dice coefficient of 58.08 % is achieved on data from a publicly available dataset.
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs. In this work, we describe our solution to INSTANCE 2022. We use a 2D segmentation network, SegResNet from MONAI, operating slice-wise without resampling. The final submission is an ensemble of 18 models. Our solution (team name NVAUTO) achieves the top place in terms of Dice metric (0.721), and overall rank 2. It is implemented with Auto3DSeg.
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 …
University of California Berkeley（加州大学伯克利分校）；University of California San Francisco School of Medicine（加州大学旧金山分校医学院）
Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets. However, the enormous cost of labeling medical data makes this challenging. In this paper, we build a cost-sensitive active learning system for the problem of intracranial hemorrhage detection and segmentation on head computed tomography (CT). We show that our ensemble method compares favorably with the state-of-the-art, while running faster and using less memory. Moreover, our experiments are done using a substantially larger dataset than earlier papers on …
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Intracranial hemorrhage occurs when blood vessels rupture or leak within the brain tissue or elsewhere inside the skull. It can be caused by physical trauma or by various medical conditions and in many cases leads to death. The treatment must be started as soon as possible, and therefore the hemorrhage should be diagnosed accurately and quickly. The diagnosis is usually performed by a radiologist who analyses a Computed Tomography (CT) scan containing a large number of cross-sectional images throughout the brain. Analysing each image manually can be very time-consuming, but automated technique…
Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning
Sberbank AI Lab（俄罗斯联邦储蓄银行人工智能实验室）；New York University（纽约大学）
Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast CT. We applied the proposed DL model to historical patient data and also conducted clinical experiments involving ten experienced radiologists. Our model achieved strong results on historical data, and significantly outperformed seven radiologist out of ten, while being on par with the remaining three.
CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking
Faculty of Computer Engineering, K. N. Toosi University of Technology（计算机工程学院，K.N. Toosi技术大学）；Faculty of Electrical Engineering, K. N. Toosi University of Technology（电气工程学院，K.N. Toosi技术大学）；Faculty of Mechanical Engineering, Tarbiat Modares University（机械工程学院，塔里亚特莫达雷斯大学）
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended t…
Data augmentation can effectively resolve a scarcity of images when training machine-learning algorithms. It can make them more robust to unseen images. We present a lesion conditional Generative Adversarial Network LcGAN to generate synthetic Computed Tomography (CT) images for data augmentation. A lesion conditional image (segmented mask) is an input to both the generator and the discriminator of the LcGAN during training. The trained model generates contextual CT images based on input masks. We quantify the quality of the images by using a fully convolutional network (FCN) score and blurrin…
Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学北京天坛医院）
CT & MRI Center（CT & MRI中心）；Mayo Clinic（梅奥诊所）；Centre for Advanced Research in Imaging, Neurosciences and Genomics（成像、神经科学和基因组学高级研究中心）
Importance: Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. Objective: To develop and validate a set of deep learning algorithms for automated detection of following key findings from non-contrast head CT scans: intracranial hemorrhage (ICH) and its types, intraparenchymal (IPH), intraventricular (IVH), subdural (SDH), extradural (EDH) and subarachnoid (SAH) hemorrhages, calvarial fractures, midline shift and mass effect. Design and Settings: We retrospectively collected a dataset containing 313,318 head CT scans along with…
AI-Powered Intracranial Hemorrhage Detection: A Co-Scale Convolutional Attention Model with Uncertainty-Based Fuzzy Integral Operator and Feature Screening
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（塔里斯坦大学电气与计算机工程学院）；Department of Computer Science and Engineering, Sejong University（世宗大学计算机科学与工程系）；Department of Neurosurgery, Iran University of Medical Sciences（伊朗医学科学大学神经外科系）
Multi-task Learning Approach for Intracranial Hemorrhage Prognosis
Advanced Computing and e-Science Group, Institute of Physics of Cantabria (IFCA), CSIC - UC（先进计算与电子科学组，坎塔布里亚物理研究所（IFCA），CSIC-UC）；AI Technology for Life, Department of Computing and Information Sciences, Utrecht University（人工智能技术与生命，计算与信息科学系，乌得勒支大学）；Department of Radiology, The Netherlands Cancer Institute（放射科，荷兰癌症研究所）；Servicio de Radiología, Hospital Reina Sofía（放射科，雷娜·索菲亚医院）；Servicio de Radiología, Hospital Universitario Rey Juan Carlos（放射科，雷昂·卡洛斯大学医院）；Departamento de Tecnologías de La Información, Universidad CEU San Pablo（信息技术部，CEU桑帕布洛大学）
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Australia Institute of Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Australian National University（澳大利亚国立大学）；Flinders Health and Medical Research Institute, Flinders University（弗林德斯大学弗林德斯健康与医学研究所）；ETH Zürich（苏黎世联邦理工学院）
Intracranial hemorrhages (ICHs) are life-threatening brain injures with a relatively high incidence. In this paper, the automatic algorithm for the detection and classification of ICHs, including localization, is present. The set of binary convolutional neural network-based classifiers with a designed cascade-parallel architecture is used. This automatic system may lead to a distinct decrease in the diagnostic process's duration in acute cases. An average Jaccard coefficient of 53.7 % is achieved on the data from the publicly available head CT dataset CQ500.
