Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional neuronal networks (CNN) proved to outperform all existent techniques in both tasks independently. However, to date, little work has been done regarding simultaneous learning of brain lesion and anatomy segmentation from disjoint datasets.
In this work we focus on training a single CNN model to predict brain tissue and lesion segmentations using heterogeneous datasets labeled independently, according to only one of these tasks (a common scenario when using publicly available datasets). We show that label contradiction issues can arise in this case, and propose a novel adaptive cross entropy (ACE) loss function that makes such training possible. We provide quantitative evaluation in two different scenarios, benchmarking the proposed method in comparison with a multi-network approach. Our experiments suggest that ACE loss enables training of single models when standard cross entropy and Dice loss functions tend to fail. Moreover, we show that it is possible to achieve competitive results when comparing with multiple networks trained for independent tasks.
GLI-AL: A Multi-Modal Glioma MRI Label Resource with Unified Anatomy-Lesion Labels
Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University（教育部生物医学信息工程重点实验室，西安交通大学生命科学与技术学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Research Center for Intelligent Medical Equipment and Devices (IMED), Xi’an Jiaotong University（西安交通大学智能医疗设备与器械研究中心）
University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；University of California, Irvine（加利福尼亚大学欧文分校）
We propose a novel, simple and effective method to integrate lesion prior and a 3D U-Net for improving brain tumor segmentation. First, we utilize the ground-truth brain tumor lesions from a group of patients to generate the heatmaps of different types of lesions. These heatmaps are used to create the volume-of-interest (VOI) map which contains prior information about brain tumor lesions. The VOI map is then integrated with the multimodal MR images and input to a 3D U-Net for segmentation. The proposed method is evaluated on a public benchmark dataset, and the experimental results show that th…
A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）；Beijing Institute of Technology（北京理工大学）；Capital Medical University（首都医科大学）；Beijing Tiantan Hospital（北京天坛医院）
Technical University of Denmark（丹麦技术大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马丁诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Technical University of Munich（慕尼黑工业大学）
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation, introducing subject-specific latent variables to encourage temporal consistency between longitudinal scans. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the …
King’s College London（伦敦国王学院）；School of Biomedical Engineering & Imaging Sciences（生物医学工程与成像科学学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民健康服务基金会信托）；NVIDIA（英伟达）；Dementia Research Centre, UCL Institute of Neurology, UCL（伦敦大学学院痴呆研究中心、伦敦大学学院神经学研究所、伦敦大学学院）；Department of Medical Physics, UCL（伦敦大学学院医学物理系）；Department of Neuroradiology（神经放射科）；St. Thomas’ Hospital（圣托马斯医院）
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently …
University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Sydney Neuroimaging Analysis Centre（悉尼神经影像分析中心）
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient's neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-obser…
iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models
Department of Radiology and Imaging Sciences, University of Arizona（亚利桑那大学放射科和影像科学系）；Department of Biomedical Engineering, Florida International University（佛罗里达国际大学生物医学工程系）；Center for Virtual Imaging Trials, Department of Radiology, Duke University Medical Center（达特茅斯大学医学中心虚拟成像试验中心，放射科）
Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenarios. Established tissue segmentation approaches have however not been developed to cope with large anatomical changes resulting from pathology. The effect of the presence of brain lesions, for example, on their performance is thus currently uncontrolled and practically unpredictable. Contrastingly, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly and is achieving …
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
University of Zurich（苏黎世大学）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学 rechts der Isar 医院）；Imperial College London（帝国理工学院）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）
Imperial College London（帝国理工学院）；Microsoft Research Cambridge（微软剑桥研究院）；Cambridge University（剑桥大学）
Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, an…
Stanford University（斯坦福大学）；Oslo University Hospital（奥斯陆大学医院）
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation. We create our ensemble by training each network on our dataset with 50% of our annotated lesions censored out. We also apply a lopsided bootstrap loss to recover performance after inducing an in silico 50% false negative rate and make our networks more sensitive. We improve our network detection of lesions's mAP value by 39% and more than triple the sensitivity at 80% precision. We also show slight improvements in segmentation quality through DICE score. Further, RB ensembli…
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classif…
Nanjing University of Information Science & Technology（南京信息工程大学）
Cerebrovascular accident, or commonly known as stroke, is an acute disease with extreme impact on patients and healthcare systems and is the second largest cause of death worldwide. Fast and precise stroke lesion detection and location is an extreme important process with regards to stroke diagnosis, treatment, and prognosis. Except from the manual segmentation approach, machine learning based segmentation methods are the most promising ones when considering efficiency and accuracy, and convolutional neural network based models are the first of its kind. However, most of these neural network m…
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）
Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on mult…
Faculty of Information Technology, University of Science, VNU-HCM（越南胡志明市国家大学科学大学信息技术学院）；SBEI School №1228 ”Lefortovo”（第1228号“列福尔托沃”基础普通教育学校）
In the development of technology, there are increasing cases of brain disease, there are more treatments proposed and achieved a positive result. However, with Brain-Lesion, the early diagnoses can improve the possibility for successful treatment and can help patients recuperate better. From this reason, Brain-Lesion is one of the controversial topics in medical images analysis nowadays. With the improvement of the architecture, there is a variety of methods that are proposed and achieve competitive scores. In this paper, we proposed a technique that uses efficient-net for 3D images, especiall…
icometrix（icometrix公司）；Technical University of Munich（慕尼黑工业大学）
Computer Tomography (CT) is the gold standard technique for brain damage evaluation after acute Traumatic Brain Injury (TBI). It allows identification of most lesion types and determines the need of surgical or alternative therapeutic procedures. However, the traditional approach for lesion classification is restricted to visual image inspection. In this work, we characterize and predict TBI lesions by using CT-derived radiomics descriptors. Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial…
Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task. State-of-the-art approaches are mostly based on supervised learning making use of large annotated datasets. Human beings, on the other hand, even non-experts, can detect most abnormal lesions after seeing a handful of healthy brain images. Replicating this capability of using prior information on the appearance of healthy brain structure to detect lesions can help computers achieve human level abnormality detection, specifically reducing the need for numerous labeled examples and bettering generalization of p…
BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation
Southern University of Science and Technology（南方科技大学）；Jarvis Research Center, Tencent YouTu Lab（腾讯优图实验室）；Westlake University（西湖大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；Tencent YouTu Lab（腾讯优图实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）深圳大数据研究院）；Sun Yat-sen University（中山大学）；Pazhou Lab（琶洲实验室）
Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation. However, locating small lesions in 2D MRI slices is challenging, and requires to balance between the granularity of 3D context aggregation and the computational complexity. In this paper, we propose a novel view-disentangled transformer to enhance the extraction of MRI features for more accurate tumour detection. First, the proposed transformer harvests long-range correlation among different positions in a 3D brain scan. Second, the transformer models a stack of slice features as multiple 2D views …
Columbia University（哥伦比亚大学）；Cold Spring Harbor Laboratory（冷泉港实验室）；Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Massachusetts Institute of Technology（麻省理工学院）；Allen Institute for Brain Science（艾伦脑科学研究所）
Reconstructing multiple molecularly defined neurons from individual brains and across multiple brain regions can reveal organizational principles of the nervous system. However, high resolution imaging of the whole brain is a technically challenging and slow process. Recently, oblique light sheet microscopy has emerged as a rapid imaging method that can provide whole brain fluorescence microscopy at a voxel size of 0.4 by 0.4 by 2.5 cubic microns. On the other hand, complex image artifacts due to whole-brain coverage produce apparent discontinuities in neuronal arbors. Here, we present connect…
Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions
Institute for Applied Life Sciences, University of Massachusetts（马萨诸塞大学应用生命科学研究所）；Manning College of Information and Computer Sciences, University of Massachusetts（马萨诸塞大学曼宁信息与计算机科学学院）；Department of Biomedical Engineering, University of Massachusetts（马萨诸塞大学生物医学工程系）；Baystate Medical Center（贝州医疗中心）；UMass Chan Medical School - Baystate Campus（马萨诸塞大学陈医学院贝州校区）；Department of Electrical and Computer Engineering, University of Massachusetts（马萨诸塞大学电气与计算机工程系）；Department of Neurology, Baystate Medical Center（贝州医疗中心神经内科）
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“伊戈尔·西科斯基基辅理工学院”）
This paper presents a new approach for relatively accurate brain region of interest (ROI) detection from dynamic susceptibility contrast (DSC) perfusion magnetic resonance (MR) images of a human head with abnormal brain anatomy. Such images produce problems for automatic brain segmentation algorithms, and as a result, poor perfusion ROI detection affects both quantitative measurements and visual assessment of perfusion data. In the proposed approach image segmentation is based on CUSUM filter usage that was adapted to be applicable to process DSC perfusion MR images. The result of segmentation…
EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models
Jilin University（吉林大学）；School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；ByteDance（字节跳动）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）
Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Learning recently has shown promising progress in many application fields, which motivates us to apply this technology for such important problem. In this paper, we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). In order to investigate the applicability, we applied our approach on several brain diseases including high and…
McGill University（麦吉尔大学）；MILA (Québec Artificial Intelligence Institute)（MILA（魁北克人工智能研究所））；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternat…
Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the researchers' attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accuracy and sensitivity, particularly in the medical field. In general, brain tumor is one of the most common and aggressive malignant tumor diseases which is leading to a very short expected life if it is diagnosed at higher grade. Based on that, brain tumor grading is a very critica…
GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT
Zhejiang Key Laboratory of Space Information Sensing and Transmission（浙江空间信息感知与传输重点实验室）；Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；Children's Hospital, Zhejiang University School of Medicine（浙江大学医学院附属儿童医院）
BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland（苏黎世大学定量生物医学系）；Helmholtz AI, Helmholtz Zentrum München, Germany（海德堡人工智能，慕尼黑研究中心）；Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany（医学诊断与介入神经放射学系，慕尼黑技术大学）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany（TranslaTUM - 转化癌症研究中央研究所，慕尼黑技术大学）；Chair for AI in Medicine, Klinikum Rechts der Isar, Munich, Germany（医学人工智能系，慕尼黑Klinikum Rechts der Isar）；Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Germany（放射肿瘤学系，慕尼黑Klinikum Rechts der Isar）；Institute of Clinical Neuroimmunology, Faculty of Medicine, Ludwig Maximilian University, Munich, Germany（临床神经免疫学研究所，路德维希-马克西米利安大学）；School of Computation, Information and Technology, Technical University of Munich, Germany（计算、信息与技术学院，慕尼黑技术大学）；Division of Computational Pathology, Indiana School of Medicine, Indianapolis, IN, USA（计算病理学系，印第安纳医学院）；Research Center for Digital Technologies in Dentistry and CAD/CAM, Department of Dentistry, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria（牙科数字技术研究中心，医学与牙科学院，多瑙私人大学）；Medical Research Group, MLCommons, San Francisco, CA, USA（医学研究组，MLCommons，旧金山，美国）；Department of Neurology, Clinical Neuroscience Center and Brain Tumor Center, University and University Hospital Zurich, 8091 Zurich, Switzerland（神经学系，临床神经科学中心和脑肿瘤中心，苏黎世大学及苏黎世大学医院）；Department of Medical Radiation Physics, Stockholm University, Solna, Sweden（医学辐射物理学系，斯德哥尔摩大学）；Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden, Sweden（肿瘤病理学系，卡罗林斯卡研究所）；Division of Oncology and Children's Research Center, University Children's Hospital Zurich, Switzerland（肿瘤与儿童研究中心，苏黎世儿童医院）；Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, USA（Wallace H. Coulter生物医学工程系，佐治亚理工学院和埃默里大学，美国）；Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Germany（放射医学研究所（IRM），海德堡研究中心（HMGU））；German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑合作伙伴站点）
AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna（医学影像与影像引导治疗系计算成像研究实验室，维也纳医学大学）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna（医学人工智能综合中心，维也纳医学大学）
University of Iceland（冰岛大学）；Landspitali - University Hospital（兰斯皮塔利大学医院）；The Icelandic Heart Association（冰岛心脏协会）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery (FLAIR) and T2-weighted magnetic resonance images (MRIs) of the human brain are common in the brains of the elderly population and may be caused by ischemia or demyelination. Lesions are biomarkers for various neurodegenerative diseases, making accurate quantification of them important for both disease diagnosis and progression. Automatic lesion detection using supervised learning requires manually annotated images, which can often be impractical to acquire. Unsupervised lesion detection, on the other hand, does not r…
Technical University of Denmark（丹麦技术大学）；Copenhagen University Hospital Hvidovre（哥本哈根大学医院维兹奥勒医院）；Medical Image Analysis Center (MIAC AG)（医学图像分析中心（MIAC AG））；Technical University of Munich（慕尼黑工业大学）；Copenhagen University Hospital Bispebjerg（哥本哈根大学医院比斯佩比约医院）；University of Copenhagen（哥本哈根大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing rob…
Imperial College London（帝国理工学院）；Cambridge University（剑桥大学）
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we ana…
Brain lesions, including stroke and tumours, have a high degree of variability in terms of location, size, intensity and form, making automatic segmentation difficult. We propose an improvement to existing segmentation methods by exploiting the bilateral quasi-symmetry of healthy brains, which breaks down when lesions are present. Specifically, we use nonlinear registration of a neuroimage to a reflected version of itself ("reflective registration") to determine for each voxel its homologous (corresponding) voxel in the other hemisphere. A patch around the homologous voxel is added as a set of…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Chung-Ang University（中央大学）
We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network. Our embedding network learns features that can resolve detailed structures in the brain without the need for high-resolution training images, which are often unavailable and expensive to acquire. Alternatively, the encoder-decoder learns global structures by means of striding and max pooling. Our embedding network complements the encode…
Beijing Institute of Technology（北京理工大学）；Peking University Third Hospital（北京大学第三医院）；Deepwise AI Lab（深度智耀人工智能实验室）
Brain lesion segmentation provides a valuable tool for clinical diagnosis, and convolutional neural networks (CNNs) have achieved unprecedented success in the task. Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other "mix"-based methods, such as Mixup and CutMix, CarveMix stochastically combines two existing labeled images to generate new…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Sree Chitra Tirunal Institute for Medical Sciences and Technology（Sree Chitra Tirunal医学科学与技术研究所）
The work presented explores the use of denoising autoencoders (DAE) for brain lesion detection, segmentation and false positive reduction. Stacked denoising autoencoders (SDAE) were pre-trained using a large number of unlabeled patient volumes and fine tuned with patches drawn from a limited number of patients (n=20, 40, 65). The results show negligible loss in performance even when SDAE was fine tuned using 20 patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach wherein a network pre-trained with High Grade Glioma (HGG) data was fine tuned using LGG im…
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
University of Pittsburgh（匹兹堡大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Cedars-Sinai Medical Center（西德萨斯医疗中心）；Mayo Clinic Arizona（梅奥诊所亚利桑那分部）
TU Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；ETH Zurich（苏黎世联邦理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for unsupervised anomaly segmentation in brain MRI have shown promising performance, but face difficulties in modeling distributions with high fidelity, which is crucial for accurate delineation of particularly small lesions. Here, similar to these previous works, we model the distribution of healthy brain MRI to localize pathologies from erroneous reconstructions. However, to achieve improved reconstruction fidelity at hi…
National Taiwan University（台湾大学）；Santa Clara University（圣克拉拉大学）；QNAP Systems, Inc（威联通科技股份有限公司）；Taipei Hospital（台北医院）；National Taiwan University Hospital（台湾大学附属医院）
Semantic segmentation of medical images with deep learning models is rapidly developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset, demonstrating the strengths and weaknesses of these algorithms in a fairly practical scenario. In particular, we compared the model performances with respect to their sampling method, model architecture, and the choice of loss functions, identifying the suitable settings for their applications and shedding light on the possible improvements.
Monitoring the progression of lesions is important for clinical response. Summary statistics such as lesion volume are objective and easy to interpret, which can help clinicians assess lesion growth or decay. CNNs are commonly used in medical image segmentation for their ability to produce useful features within large contexts and their associated efficient iterative patch-based training. Many CNN architectures require hundreds of thousands parameters to yield a good segmentation. In this work, an efficient, computationally inexpensive CNN is implemented to estimate the number of lesion voxels…
Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images. Their full acceptance by clinicians remains however hampered by the lack of intelligible uncertainty assessment of the provided results. Most approaches to quantify their uncertainty, such as the popular Monte Carlo dropout, restrict to some measure of uncertainty in prediction at the voxel level. In addition not to be clearly related to genuine medical uncertainty, this is not clinically satisfying as most objects of interest (e.g. brain lesions) are made of groups of voxels whose …
