Accurate segmentation of brain tumors is vital for diagnosis, surgical planning, and treatment monitoring. Deep learning has advanced on benchmarks, but two issues limit clinical use: no uncertainty estimates for errors and no segmentation of healthy brain structures around tumors for surgery. Current methods fail to unify tumor localization with anatomical context and lack confidence scores. This study presents an uncertainty-aware framework augmenting nnUNet with a channel for voxel-wise uncertainty. Trained on BraTS2023, it yields a correlation of 0.750 and RMSD of 0.047 for uncertainty without hurting tumor accuracy. It predicts uncertainty in one pass, with no extra networks or inferences, aiding clinical decisions. For whole-brain context, a unified model combines normal and cancer datasets, achieving a DSC of 0.81 for brain structures and 0.86 for tumor, with robust key-region performance. Combining both innovations gives the first model outputting tumor in natural surroundings plus an overlaid uncertainty map. Visual checks of outputs show uncertainty offers key insights to evaluate predictions and fix errors, helping informed surgical decisions from AI.
Zhejiang University（浙江大学）；Massachusetts General Hospital（麻省总医院）；Zhejiang University of Technology（浙江工业大学）
Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical location and heterogeneous appearance on MRI. In this study, we propose deep interactive networks (DINs) to address the above limitations. User interactions guide the model to recognize complicated tumors and quickly a…
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment
Viterbi School of Engineering（维特比工程学院）；College of Engineering（工程学院）；University of Southern California（南加州大学）；Rochester Institute of Technology（罗切斯特理工学院）；Northeastern University（东北大学）；School of Arts & Sciences（艺术与科学学院）；State University of New York Plattsburgh（纽约州立大学普拉茨堡分校）
A Synthetic Data-Driven Radiology Foundation Model for Pan-tumor Clinical Diagnosis
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Lab（上海人工智能实验室）；The First Hospital of China Medical University（中国医科大学附属第一医院）；Harvard Medical School（哈佛医学院）
TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth
Technical University of Munich (TUM), Germany（慕尼黑技术大学（TUM））；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；AI for Image-Guided Diagnosis and Therapy（图像引导诊断与治疗的AI）；Chair for AI in Healthcare and Medicine, TUM and TUM University Hospital, Munich, Germany（医疗与医学中的AI主任，慕尼黑技术大学及慕尼黑大学医院）
TumorChain: Interleaved Multimodal Chain-of-Thought Reasoning for Traceable Clinical Tumor Analysis
Zhejiang University（浙江大学）；DAMO Academy, Alibaba Group（阿里集团达摩院）；Hupan Lab（虎扑实验室）；Shanghai Institution of Pancreatic Disease（上海胰腺疾病研究所）；Shengjing Hospital of China Medical University（中国医科大学盛京医院）；Sun Yat-sen University Cancer Center（中山大学肿瘤中心）
Sanofi（赛诺菲）；Steven Institute of Technology（史蒂文斯理工学院）
Failures in Phase 3 clinical trials contribute to expensive cost of drug development in oncology. To drastically reduce such cost, responders to an oncology treatment need to be identified early on in the drug development process with limited amount of patient data before the planning of Phase 3 clinical trials. Despite the challenge of small sample size, we pioneered the use of deep-learning derived digital pathology scores to identify responders based on the immunohistochemistry images of the target antigen expressed in tumor biopsy samples from a Phase 1 Non-small Cell Lung Cancer clinical …
Clinical diagnostic and treatment decisions rely upon the integration of patient-specific data with clinical reasoning. Cancer presents a unique context that influence treatment decisions, given its diverse forms of disease evolution. Biomedical imaging allows noninvasive assessment of disease based on visual evaluations leading to better clinical outcome prediction and therapeutic planning. Early methods of brain cancer characterization predominantly relied upon statistical modeling of neuroimaging data. Driven by the breakthroughs in computer vision, deep learning became the de facto standar…
BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features
University of Washington（华盛顿大学）；University of Washington School of Medicine（华盛顿大学医学院）；Microsoft Health AI（微软健康人工智能）；Johns Hopkins School of Medicine（约翰霍普金斯医学院）
The Diagnosis a Reporter Leaves Unspoken: Surfacing Frozen Tumor Features for Brain-Tumor MRI Reporting
National University of Sciences and Technology（国立科技大学）；Consultant Radiologist（顾问放射医师）；Bahria University Health Sciences Campus（巴里亚大学健康科学校区）；Faculty of Science, Ontario Tech University（安大略理工大学理学院）
Anatomy-Informed Deep Learning and Radiomics for Automated Neurofibroma Segmentation in Whole-Body MRI
Institute for Applied Medical Informatics（应用医学信息学研究所）；Institute of Computational Neuroscience（计算神经科学研究所）；Center for Biomedical Artificial Intelligence (bAIome)（生物医学人工智能中心）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine（诊断与介入放射学及核医学部门）；Department of Neurology（神经内科部门）
Future Processing Healthcare（未来医疗处理公司）；Silesian University of Technology（西里西亚工业大学）；Medical University of Silesia in Katowice（卡托维兹西里西亚医科大学）；ZSM Chorzów（霍茹夫ZSM医院）；University Clinical Centre（大学临床中心）；Roche Pharmaceutical Research & Early Development（罗氏制药研究与早期开发部）
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimens…
NeuroRAD-FM: A Foundation Model for Neuro-Oncology with Distributionally Robust Training
Department of Biomedical Informatics, Stony Brook University（生物医学信息学系，石溪大学）；Department of Radiology, Columbia University Irving Medical Center（放射学系，哥伦比亚大学伊万杰琳医疗中心）；Department of Neuro-Oncology, Columbia University Irving Medical Center（神经肿瘤学系，哥伦比亚大学伊万杰琳医疗中心）
Wuhan Children’s Hospital affiliated with Tongji Medical College（同济医学院附属武汉儿童医院）；Huazhong University of Science and Technology（华中科技大学）
Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, we adopt multiple machine learning (ML) algorithms to predict the presence or absence of MYCN gene amplification. The dataset is composed of retrospective CT images of 23 neuroblastoma patients. Different from previous work, we develop the algorithm without manually-segmente…
Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI
Universitat Politècnica de Catalunya (UPC)（西班牙巴塞罗那理工大学）；University College Dublin (UCD)（都柏林大学）；Dublin City University (DCU)（都柏林城市大学）；Insight Research Ireland Center For Data Analytics（爱尔兰数据分析研究中心）
PAII Inc.（PAII公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Changhai Hospital（长海医院）；Ping An Technology Co., Ltd.（平安科技（深圳）有限公司）
Accurate and automated tumor segmentation is highly desired since it has the great potential to increase the efficiency and reproducibility of computing more complete tumor measurements and imaging biomarkers, comparing to (often partial) human measurements. This is probably the only viable means to enable the large-scale clinical oncology patient studies that utilize medical imaging. Deep learning approaches have shown robust segmentation performances for certain types of tumors, e.g., brain tumors in MRI imaging, when a training dataset with plenty of pixel-level fully-annotated tumor images…
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院阿蒂努拉·A·马蒂诺斯生物医学成像中心）；Faculty of Engineering, University of Porto（波尔图大学工程学院）；Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)（系统与计算机工程、技术与科学研究院）
University of Central Florida（中佛罗里达大学）；University of Engineering and Technology, Taxila（塔克西拉工程技术大学）；University of Wah（瓦赫大学）；Lahore University of Management Sciences（拉合尔管理科学大学）
Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistancein diagnosis of cancer, planning of treatment strategy, and predictionof survival. Radiomics in neuro-oncology has progressed significantly inthe recent past. Deep learning has outperformed conventional machinelearning methods in most image-based applications. Convolutional neu-ral networks (CNNs) have seen some popularity in radiomics, since theydo not require hand-crafted features and can automatically extract fea-tures during the learning process. In this reg…
Johns Hopkins University（约翰斯·霍普金斯大学）；National Tsing Hua University（清华大学（台湾））；PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；Ping An Technology（平安科技）
Determining the spread of GTV$_{LN}$ is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy for many cancers. Different from the more common enlarged lymph node (LN), GTV$_{LN}$ also includes smaller ones if associated with high positron emission tomography signals and/or any metastasis signs in CT. This is a daunting task. In this work, we propose a unified LN appearance and inter-LN relationship learning framework to detect the true GTV$_{LN}$. This is motivated by the prior clinical knowledge that LNs form…
The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations between centers and the need to estimate visually the staining intensity in specific percentages of tumor area. In this paper, focusing on the interpretability of HER2 scoring by a pathologist, we propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines defined by the American Society of Clinic…
The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process that can be overcome with the help of computerized segmentation methods. In this contribution, two methods for World Health Organization (WHO) grade IV glioma segmentation in the human brain are compared using magnetic resonance imaging (MRI) patient data from the clinical routine. One method uses balloon infla…
Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm
Georgia Institute of Technology（佐治亚理工学院）；Columbia University Medical Center（哥伦比亚大学医学中心）；Mayo Clinic Arizona（妙佑医疗国际亚利桑那分院）；Barrow Neurological Institute - St. Joseph’s Hospital and Medical Center（巴罗神经学研究所 - 圣约瑟夫医院和医疗中心）；University of Naples “Federico II”（那不勒斯“费德里科二世”大学）
Deep learning algorithms have accounted for the rapid acceleration of research in artificial intelligence in medical image analysis, interpretation, and segmentation with many potential applications across various sub disciplines in medicine. However, only limited number of research which investigates these application scenarios, are deployed into the clinical sector for the evaluation of the real requirement and the practical challenges of the model deployment. In this research, a deep convolutional neural network (CNN) based classification network and Faster RCNN based localization network w…
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Children’s Hospital of Philadelphia（费城儿童医院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；National Cancer Institute（美国国家癌症研究所）；Children's National Hospital（儿童国家医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎耶德儿科外科创新研究院）
PAII Inc.（PAII公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；Chang Gung Memorial Hospital（长庚纪念医院）；National Tsing Hua University（国立清华大学）；The First Affiliated Hospital Zhejiang University（浙江大学医学院附属第一医院）；Ping An Technology（平安科技）
Finding and identifying scatteredly-distributed, small, and critically important objects in 3D oncology images is very challenging. We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task. Determining and delineating the spread of OSLNs is essential in defining the corresponding resection/irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. For patients who are treated with radiotherapy, this task is performed by ex…
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
King's College London（伦敦国王学院）；St. Thomas' Hospital（圣托马斯医院）；King's College Hospital NHS Foundation Trust（国王学院医院NHS基金会信托）
Imaging biomarkers in neuro-oncology are used for diagnosis, prognosis and treatment response monitoring. Magnetic resonance imaging is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Following image feature extraction, machine learning allows accurate classification in a variety of scenarios. Machine learning also enables image feature extraction de novo although the low prevalence of brain tumours makes such approaches challenging. Much r…
Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
Program of Computational Biology and Bioinforamtics, Yale University（耶鲁大学计算生物学与生物信息学项目）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学博德研究所）；Department of Statistics and Data Science, Northwestern University（西北大学统计与数据科学系）；Department of Computer Science, Northeastern University（东北大学计算机科学系）；Department of Computer Science, Harvard University（哈佛大学计算机科学系）；Department of Pathology, Yale University（耶鲁大学病理学系）；Department of Anatomic Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania（宾夕法尼亚大学医院解剖病理学与检验医学系）；Department of Pathology and Laboratory Medicine, University of California, San Francisco（加州大学旧金山分校病理学与检验医学系）；Department of Pathology and Laboratory Medicine, KK Women’s and Children’s Hospital（竹脚妇幼医院病理学与检验医学系）；Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院生物统计学、流行病学与信息学系）
NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI
Lucian Blaga University of Sibiu（锡比乌卢奇安·布拉加大学）；University of Arizona（亚利桑那大学）；Pediatric Clinical Hospital of Sibiu（锡比乌儿科临床医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；MedLife Polisano Hospital（MedLife Polisano医院）
The Johns Hopkins University（约翰斯·霍普金斯大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. To provide radiologists with tumor locations, we adopt a segmentation framework to classify CT volumes by checking if at least a sufficient number of voxels is segmented as tumors. To quantitatively analyze our method, we collect and voxelwisely l…
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
College of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院，深圳大学）；Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa（海军、电气、电子与电信工程部门，热那亚大学）
MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis
Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Center for Brain-Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University（脑启发计算研究中心（CBICR）、精密仪器系，清华大学）
Multimodal classification of Radiation-Induced Contrast Enhancements and tumor recurrence using deep learning
Deutsches Krebsforschungszentrum (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Heidelberg University Hospital（海德堡大学医院）；Research Campus M2OLIE（M2OLIE研究园区）；German Cancer Consortium (DKTK)（德国癌症联盟）；Universitätsklinikum Heidelberg（海德堡大学医院）；Heidelberger Institut für Radioonkologie (HIRO)（海德堡放射肿瘤学研究所）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberger Ionenstrahl-Therapiezentrum (HIT)（海德堡离子束治疗中心）；Universitätsklinikum Heidelberg, Neurochirurgie（海德堡大学医院神经外科）
Learning Brain Tumor Representation in 3D High-Resolution MR Images via Interpretable State Space Models
Stony Brook University（石溪大学）；University of Rochester（罗切斯特大学）；Technical University of Munich（慕尼黑工业大学）；Southern University of Science and Technology（南方科技大学）；Harvard Medical School（哈佛医学院）
Arizona State University（亚利桑那州立大学）；Barrow Neurological Institute（巴罗神经学研究所）；St. Joseph’s Hospital and Medical Center（圣约瑟夫医院与医疗中心）；Irkutsk State Medical University（伊尔库茨克州立医科大学）
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraope…
Magnetic Resonance Imaging (MRI) is an important diagnostic tool for precise detection of various pathologies. Magnetic Resonance (MR) is more preferred than Computed Tomography (CT) due to the high resolution in MR images which help in better detection of neurological conditions. Graphical user interface (GUI) aided disease detection has become increasingly useful due to the increasing workload of doctors. In this proposed work, a novel two steps GUI technique for brain tumor segmentation as well as Brodmann area detec-tion of the segmented tumor is proposed. A data set of T2 weighted images …
Institute for Surgical Technology and Biomechanics (ISTB)（外科技术与生物力学研究所（ISTB））；University of Bern（伯尔尼大学）；Support Center for Advanced Neuroimaging (SCAN)（高级神经影像支持中心（SCAN））；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）；University Clinic for Radio-oncology（放射肿瘤学大学诊所）；Bern University Hospital（伯尔尼大学医院）
Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlying model. Reliable uncertainties are particularly interesting for safety-critical computer-assisted applications in medicine, e.g., neurosurgical interventions and radiotherapy planning. We propose an uncertainty-driven sanity check for the identification of segmentation results that need particular expert review. Our method uses a fully-convolutional neural network and computes uncertainty estimates by the principle…
Intelligent Systems in Neuroimaging: Pioneering AI Techniques for Brain Tumor Detection
Dept.of ECE North South University Dhaka, Bangladesh（电子与计算机工程系北南大学达卡，孟加拉国）；NMR Spectroscopist Lassonde School of Engineering York University Toronto, Canada（核磁共振谱仪师拉索nde工程学院约克大学多伦多，加拿大）；Dept. of ECE North South University Dhaka, Bangladesh（电子与计算机工程系北南大学达卡，孟加拉国）
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation
School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Shanghai Medical College, Fudan University Shanghai Cancer Center（复旦大学上海医学院附属肿瘤医院）；School of Computer Science, University of Illinois（伊利诺伊大学计算机科学学院）
Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
ETH Zurich, 8092 Zurich, Switzerland - SIB Swiss Institue of Bioinformatics, 1015 Lausanne, Switzerland Department of Radiation Oncology, Inselspital, Bern University Hospital；University of Bern, Switzerland Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Medical Center, Washington, DC 20010, United States University of California San Francisco, San Francisco, CA 94143, United States University Children's Hospital Zurich, 8008 Zurich, Switzerland
Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
Department of Communication Engineering University of Málaga（通信工程系马德里大学）；Department of Clinical Neurosciences University of Cambridge（临床神经科学系剑桥大学）；Department of Neuroscience University of Padova（神经科学系帕多瓦大学）；Department of Signal Theory, Telematics and Communications, University of Granada（信号理论、电信与通信系格拉纳达大学）；Department of Computer Science and Technology University of Cambridge（计算机科学与技术系剑桥大学）；Department of Psychiatry University of Cambridge（精神病学系剑桥大学）；NHS Foundation Trust United Kingdom（国家健康服务基金会信托英国）
Hunan University（湖南大学）；Central South University（中南大学）；Third Xiangya Hospital（湘雅三医院）
Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative approaches to patient prognosis. In the past decade, rapid advances in artificial intelligence, chip design and manufacturing, and mobile computing have facilitated research in computational pathology and have the potential to provide better-integrated solutions for whole-slide im…
Gliomas are among the most aggressive and deadly brain tumors. This paper details the proposed Deep Neural Network architecture for brain tumor segmentation from Magnetic Resonance Images. The architecture consists of a cascade of three Deep Layer Aggregation neural networks, where each stage elaborates the response using the feature maps and the probabilities of the previous stage, and the MRI channels as inputs. The neuroimaging data are part of the publicly available Brain Tumor Segmentation (BraTS) 2020 challenge dataset, where we evaluated our proposal in the BraTS 2020 Validation and Tes…
Concordia University（康考迪亚大学）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Western University（韦仕敦大学）；St. Olavs Hospital（圣奥拉夫医院）；Trondheim University Hospital（特隆赫姆大学医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；SINTEF Digital（SINTEF数字研究所）
Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images play an essential role in surgical planning and resection of brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique annotation dataset of tumor tissues and resection cavities from the previously published RESECT dataset (Xiao et al. 2017) to encourage a more rigorous assessments of image processing techniques. Acquisition and validation methods: The RESECT database …
