Past few years have witnessed the prevalence of deep learning in many application scenarios, among which is medical image processing. Diagnosis and treatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper, a 3D U-net based deep learning model has been trained with the help of brain-wise normalization and patching strategies for the brain tumor segmentation task in the BraTS 2019 competition. Dice coefficients for enhancing tumor, tumor core, and the whole tumor are 0.737, 0.807 and 0.894 respectively on the validation dataset. These three values on the test dataset are 0.778, 0.798 and 0.852. Furthermore, numerical features including ratio of tumor size to brain size and the area of tumor surface as well as age of subjects are extracted from predicted tumor labels and have been used for the overall survival days prediction task. The accuracy could be 0.448 on the validation dataset, and 0.551 on the final test dataset.
University of Tehran（德黑兰大学）；University of Nottingham（诺丁汉大学）
In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classi-fication algorithm based on random forests, for survival prediction is presented. The objective is to segment the glioma area and produce segmentation labels for its different sub-regions, i.e. necrotic and the non-enhancing tumor core, the peri-tumoral edema, and enhancing tumor. The proposed deep architecture for the segmentation task encompasses two parallel streamlines with two different reso-lutions. One deep convolutional neural net…
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）；Fudan University（复旦大学）；Royal Prince Alfred Hospital（皇家阿尔弗雷德亲王医院）
Accurate segmentation of different sub-regions of gliomas including peritumoral edema, necrotic core, enhancing and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape, segmentation of the sub-regions is very challenging. Recent development using deep learning models has proved its effectiveness in the past several brain segmentation challenges as well as other semantic and medical image segmentation problems. Most models in brain tumor segmentati…
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Institute for Systems and Robotics, Instituto Superior Tecnico（高等理工学院系统与机器人研究所）
Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOTA) methods for OS time prediction follow a pre-hoc approach that require computing the segmentation map of the glioma tumor sub-regions (necrotic, edema tumor, enhancing tumor) for estimating OS time. However, the training of the segmentation methods require ground truth segmentation labels which are tedious and expensive to obtain. Given that most of the large-scale data sets available from hospitals are unlikely to…
Southern Methodist University（南卫理公会大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Tumor shape is a key factor that affects tumor growth and metastasis. This paper proposes a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examines its effect on the time-to-event data. The proposed topological features are invariant to scale-preserving transformation and can summarize various tumor shape patterns. The topological features are represented in functional space and used as functional predictors in a functional Cox proportional hazards model. The proposed model enables interpretable inference ab…
Concordia Institute for Information Systems Engineering, Concordia University（康考迪亚大学康考迪亚信息系统工程研究所）；University of Toronto（多伦多大学）
According to official statistics, cancer is considered as the second leading cause of human fatalities. Among different types of cancer, brain tumor is seen as one of the deadliest forms due to its aggressive nature, heterogeneous characteristics, and low relative survival rate. Determining the type of brain tumor has significant impact on the treatment choice and patient's survival. Human-centered diagnosis is typically error-prone and unreliable resulting in a recent surge of interest to automatize this process using convolutional neural networks (CNNs). CNNs, however, fail to fully utilize …
Institute for Surgical Technology and Biomechanics, University of Bern（伯尔尼大学外科技术与生物力学研究所）；University Clinic for Radio-oncology, Inselspital, Bern University Hospital, University of Bern（伯尔尼大学伯尔尼大学医院因塞尔医院放射肿瘤学大学诊所）；Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern（伯尔尼大学因塞尔医院诊断与介入神经放射学大学研究所高级神经成像支持中心）
Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments wer…
The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）
Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to foc…
Image-based brain cancer prediction models, based on radiomics, quantify the radiologic phenotype from magnetic resonance imaging (MRI). However, these features are difficult to reproduce because of variability in acquisition and preprocessing pipelines. Despite evidence of intra-tumor phenotypic heterogeneity, the spatial diversity between different slices within an MRI scan has been relatively unexplored using such methods. In this work, we propose a deep anchor attention aggregation strategy with a Vision Transformer to predict survival risk for brain cancer patients. A Deep Anchor Attentio…
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Maastricht University（马斯特里赫特大学）
BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
Department of Radiology \& Biomedical Imaging, Department of Biomedical Engineering, Department of Electrical Engineering, Department of Statistics \& Data Science Yale University, New Haven, CT, 06510, USA
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）
Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
Division AI4Health, Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学AI4Health部门）；University Institute for Diagnostic and Interventional Radiology, Klinikum Oldenburg AöR（奥尔登堡医院大学诊断与介入放射学研究所）
NUS Graduate School for Integrative Sciences and Engineering, NUS（新加坡国立大学综合科学与工程研究生院）；National University of Singapore（新加坡国立大学）；NIT, Tiruchirappalli（蒂鲁吉拉伯利国立理工学院）；University of Moratuwa（莫拉图瓦大学）；Punjab Engineering College（旁遮普工程学院）
In this work, we develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using various machine learning methods. We adopt a 3D UNet architecture and integrate channel and spatial attention with the decoder network to perform segmentation. For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient. We also perform extensive experiments t…
Glioblastoma Overall Survival Prediction With Vision Transformers
Department of Electronic, Information and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工大学）；Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta（放射学系，IRCCS神经科学卡洛·贝斯塔研究所）
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Intel Technology India Pvt. Ltd（英特尔技术印度私人有限公司）
We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connectivity patterns to reduce the number of weights and residual connections and is initialized with weights obtained from training this model with BraTS 2018 dataset. Hard mining is done during training to train for the difficult cases of segmentation tasks by increasing the dice similarity coefficient (DSC) threshold to choose the hard cases as epoch increases. On the BraTS2020 validation data (n = 125), this architect…
Brain tumor segmentation intends to delineate tumor tissues from healthy brain tissues. The tumor tissues include necrosis, peritumoral edema, and active tumor. In contrast, healthy brain tissues include white matter, gray matter, and cerebrospinal fluid. The MRI based brain tumor segmentation research is gaining popularity as; 1. It does not irradiate ionized radiation like X-ray or computed tomography imaging. 2. It produces detailed pictures of internal body structures. The MRI scans are input to deep learning-based approaches which are useful for automatic brain tumor segmentation. The fea…
FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition
The Hong Kong University of Science and Technology（香港科技大学）；Tencent AI Lab（腾讯AI实验室）；Harvard University（哈佛大学）；The Chinese University of Hong Kong（香港中文大学）；The Third Affiliated Hospital of Southern Medical University（南方医科大学第三附属医院）；Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University（中山大学孙逸仙纪念医院）；The University of Hong Kong（香港大学）；Shenzhen-Hong Kong Collaborative Innovation Research Institute（深港产学研协同创新研究院）
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
When oncologists estimate cancer patient survival, they rely on multimodal data. Even though some multimodal deep learning methods have been proposed in the literature, the majority rely on having two or more independent networks that share knowledge at a later stage in the overall model. On the other hand, oncologists do not do this in their analysis but rather fuse the information in their brain from multiple sources such as medical images and patient history. This work proposes a deep learning method that mimics oncologists' analytical behavior when quantifying cancer and estimating patient…
National University of Singapore（新加坡国立大学）；National Institute of Technology, Tiruchirappalli（蒂鲁吉拉伯利国立理工学院）
Segmentation of brain tumor from magnetic resonance imaging (MRI) is a vital process to improve diagnosis, treatment planning and to study the difference between subjects with tumor and healthy subjects. In this paper, we exploit a convolutional neural network (CNN) with hypercolumn technique to segment tumor from healthy brain tissue. Hypercolumn is the concatenation of a set of vectors which form by extracting convolutional features from multiple layers. Proposed model integrates batch normalization (BN) approach with hypercolumn. BN layers help to alleviate the internal covariate shift duri…
Automated brain tumor segmentation plays an important role in the diagnosis and prognosis of the patient. In addition, features from the tumorous brain help in predicting patients overall survival. The main focus of this paper is to segment tumor from BRATS 2018 benchmark dataset and use age, shape and volumetric features to predict overall survival of patients. The random forest classifier achieves overall survival accuracy of 59% on the test dataset and 67% on the dataset with resection status as gross total resection. The proposed approach uses fewer features but achieves better accuracy th…
Glioma, the malignant brain tumor, requires immediate treatment to improve the survival of patients. Gliomas heterogeneous nature makes the segmentation difficult, especially for sub-regions like necrosis, enhancing tumor, non-enhancing tumor, and Edema. Deep neural networks like full convolution neural networks and ensemble of fully convolution neural networks are successful for Glioma segmentation. The paper demonstrates the use of a 3D fully convolution neural network with a three layer encoder decoder approach for layer arrangement. The encoder blocks include the dense modules, and decoder…
City University of Hong Kong（香港城市大学）；Kwong Wah Hospital（广华医院）
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, prognosis and treatment of gliomas. However, due to the high degree variations of heterogeneous appearance and individual physical state, the segmentation of sub-regions and OS prediction are very challenging. To deal with these challenges, we utilize a 3D dilated multi-fiber network (DMFNet) with weighted dice loss for brain tumor segmentation, which incorporates prior volume …
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively captur…
Department of Health Technology, Technical University of Denmark（丹麦技术大学健康技术系）；Department of Oncology, The Finsen Center, Rigshospitalet（哥本哈根大学医院芬森中心肿瘤学系）；Department of Clinical Physiology, Nuclear Medicine and PET, Center of Diagnostic Investigation, Rigshospitalet（哥本哈根大学医院诊断研究中心临床生理学、核医学与正电子发射断层扫描科）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院Athinoula A. Martinos生物医学成像中心）
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct biological interpretation: They measure the…
University of Sydney（悉尼大学）；Fudan University（复旦大学）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）
Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also in…
The paper demonstrates the use of the fully convolutional neural network for glioma segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture is used along with dense connection at encoder part to propagate the information from coarse layer to deep layers. This architecture is used to train three tumor sub-components separately. Subcomponent training weights are initialized with whole tumor weights to get the localization of the tumor within the brain. At the end, three segmentation results were merged to get the entire tumor segmentation. Dice Similarity of traini…
PAII Inc.（PAII 公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Ping An Technology（平安科技）
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis. Surgery remains the best chance of a potential cure for patients who are eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients of the same stage and received similar treatments. Accurate preoperative prognosis of resectable PDACs for personalized treatment is thus highly desired. Nevertheless, there are no automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC. Tumor attenuation chang…
School of Engineering and Applied Science, Ahmedabad University（艾哈迈达巴德大学工程与应用科学学院）
Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results. The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various mode…
Case Western Reserve University（凯斯西储大学）；Picture Health（Picture Health公司）；Stony Brook University（石溪大学）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医学中心）；Yale School of Medicine（耶鲁医学院）；The Cleveland Clinic Foundation (CCF)（克利夫兰诊所基金会（CCF））；New York University (NYU) Langone Medical Center（纽约大学（NYU）朗格尼医学中心）；Louis Stokes Cleveland Veterans Medical Center（路易斯·斯托克斯克利夫兰退伍军人医学中心）
Purpose: Tumor-associated vasculature differs from healthy blood vessels by its chaotic architecture and twistedness, which promotes treatment resistance. Measurable differences in these attributes may help stratify patients by likely benefit of systemic therapy (e.g. chemotherapy). In this work, we present a new category of radiomic biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancers, imaging modalities, and treatment regimens. Experimental Design: We segmented tumor vessels and …
Macquarie University（麦考瑞大学）；Australian Institute of Health Innovation（澳大利亚健康创新研究院）
Pre-processing and Data Augmentation play an important role in Deep Convolutional Neural Networks (DCNN). Whereby several methods aim for standardization and augmentation of the dataset, we here propose a novel method aimed to feed DCNN with spherical space transformed input data that could better facilitate feature learning compared to standard Cartesian space images and volumes. In this work, the spherical coordinates transformation has been applied as a preprocessing method that, used in conjunction with normal MRI volumes, improves the accuracy of brain tumor segmentation and patient overa…
Learning latent progression states from spatial heterogeneity in uterine histopathology
Fuzhou University（福州大学）；Fuzhou University Affiliated Provincial Hospital（福州大学附属省立医院）；Interdisciplinary Institute for Medical Engineering, Fuzhou University（福州大学医学工程交叉研究院）；Medical Optical Technology R&D Center, Research Institute of Tsinghua, Pearl River Delta（清华珠三角研究院医学光学技术研发中心）；Peking University Health Science Center（北京大学医学部）；Third Hospital, School of Basic Medical Sciences, Peking University Health Science Center（北京大学医学部基础医学院第三医院）；Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院生物医药与健康工程研究院）；Tsinghua University（清华大学）；Peng Cheng Laboratory（鹏城实验室）；Jinfeng Laboratory（金凤实验室）
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）
Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes. Also, the weak interpretability of the model poses challenges to clinical application. Here we proposed a machine learning framework to semi-automatically fine-tune the clustering algorithms and quantitatively identify stable sub-re…
UET Taxila（塔克西拉工程技术大学）；University of Central Florida（中佛罗里达大学）
Surgery planning in patients diagnosed with brain tumor is dependent on their survival prognosis. A poor prognosis might demand for a more aggressive treatment and therapy plan, while a favorable prognosis might enable a less risky surgery plan. Thus, accurate survival prognosis is an important step in treatment planning. Recently, deep learning approaches have been used extensively for brain tumor segmentation followed by the use of deep features for prognosis. However, radiomics-based studies have shown more promise using engineered/hand-crafted features. In this paper, we propose a three-st…
Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image feat…
Inception Institute of Artificial Intelligence（人工智能起源研究所）；Lehigh University（利哈伊大学）；Tianjin University（天津大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Although many OS time prediction methods have been developed and obtain promising results, there are still several issues. First, conventional prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume, which may not represent the full image or model complex tumor patterns. Second, different types of scanners (i.e., multi-modal data) are sensitive to different brain regions, which makes it challenging to effectively ex…
University Institute of Diagnostic and Interventional Neuroradiology（诊断与介入神经放射学大学研究所）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）
The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by separated training on low-grade gliomas (LGGs) and high-grade gliomas (HGGs), but in practice this information is not available at test time to decide which model to use. By contrast with HGGs, LGGs often present no sharp boundary between the tumor core and the surrounding edema, but rather a gradual reduction of tumor-cell density. Utilizing our 3D-to-2D fully convolutional architecture, DeepSCAN, which ranked highl…
The brain tumor is the most aggressive kind of tumor and can cause low life expectancy if diagnosed at the later stages. Manual identification of brain tumors is tedious and prone to errors. Misdiagnosis can lead to false treatment and thus reduce the chances of survival for the patient. Medical resonance imaging (MRI) is the conventional method used to diagnose brain tumors and their types. This paper attempts to eliminate the manual process from the diagnosis process and use machine learning instead. We proposed the use of pretrained convolutional neural networks (CNN) for the diagnosis and …
We proposed a fully automatic workflow for glioblastoma (GBM) survival prediction using deep learning (DL) methods. 285 glioma (210 GBM, 75 low-grade glioma) patients were included. 163 of the GBM patients had overall survival (OS) data. Every patient had four pre-operative MR scans and manually drawn tumor contours. For automatic tumor segmentation, a 3D convolutional neural network (CNN) was trained and validated using 122 glioma patients. The trained model was applied to the remaining 163 GBM patients to generate tumor contours. The handcrafted and DL-based radiomic features were extracted …
Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognostic biomarkers for ccRCC. Methods: We proposed a weakly-supervised deep learning strategy using conventional histology of 1752 whole slide images from multiple centers. Our study was demonstrated through internal cross-validation and external validations for the deep learning-based models. Results: Automatic diagnosis for ccRCC through intelligent subtyping of renal cell carcinoma was proved in this study. Our graderis…
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor Segmentation Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2) Prediction of patient overall survival (OS) from pre-operative MRI scans; 3) Distinction of true tumor recurrence from treatment related effects …
Glioblastomas are the most common malignant brain tumors in adults. Approximately 200000 people die each year from Glioblastoma in the world. Glioblastoma patients have a median survival of 12 months with optimal therapy and about 4 months without treatment. Glioblastomas appear as heterogeneous necrotic masses with irregular peripheral enhancement, surrounded by vasogenic edema. The current standard of care includes surgical resection, radiotherapy and chemotherapy, which require accurate segmentation of brain tumor subregions. For effective treatment planning, it is vital to identify the met…
Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the orig…
Today, over 700,000 people are living with brain tumors in the United States. Brain tumors can spread very quickly to other parts of the brain and the spinal cord unless necessary preventive action is taken. Thus, the survival rate for this disease is less than 40% for both men and women. A conclusive and early diagnosis of a brain tumor could be the difference between life and death for some. However, brain tumor detection and segmentation are tedious and time-consuming processes as it can only be done by radiologists and clinical experts. The use of computer vision techniques, such as Mask R…
Neuromation OU；University of Oulu（奥卢大学）；MIT（麻省理工学院）；University of Michigan（密歇根大学）；Steklov Mathematical Institute（斯捷克洛夫数学研究所）
Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor's response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient's survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment…
University of Iowa（爱荷华大学）；Stanford University（斯坦福大学）
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed tomography (PET/CT) images have predictive power on NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neu…
Hamburg University of Technology（汉堡工业大学）；Carl von Ossietzky University Oldenburg（奥尔登堡大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；University of Bonn（波恩大学）；Klinikum Braunschweig（不伦瑞克医院）
Early detection of cancerous tissue is crucial for long-term patient survival. In the head and neck region, a typical diagnostic procedure is an endoscopic intervention where a medical expert manually assesses tissue using RGB camera images. While healthy and tumor regions are generally easier to distinguish, differentiating benign and malignant tumors is very challenging. This requires an invasive biopsy, followed by histological evaluation for diagnosis. Also, during tumor resection, tumor margins need to be verified by histological analysis. To avoid unnecessary tissue resection, a non-inva…
Segmentation of head and neck (H\&N) tumours and prediction of patient outcome are crucial for patient's disease diagnosis and treatment monitoring. Current developments of robust deep learning models are hindered by the lack of large multi-centre, multi-modal data with quality annotations. The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods of the primary gross target volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography images and prediction of progression-free survival in H\&N oropharynge…
