Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-Attenuated Inversion Recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture; namely DeepSeg for fully automated detection and segmentation of the brain lesion using FLAIR MRI data.
Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full resolution probability map. Based on modified U-Net architecture, different CNN models such as Residual Neural Network (ResNet), Dense Convolutional Network (DenseNet), and NASNet have been utilized in this study.
Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of Brain Tumor Segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly.
Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open-source and freely available at https://github.com/razeineldin/DeepSeg/.
Reutlingen University（罗伊特林根大学）；Menoufia University（梅努菲亚大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Glioblastomas are the most aggressive fast-growing primary brain cancer which originate in the glial cells of the brain. Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation. The Brain Tumor Segmentation Challenge (BraTS) has been a popular benchmark for automatic brain glioblastomas segmentation algorithms since its initiation. In this year, BraTS 2021 challenge provides the largest multi-parametric (mpMRI) dataset of 2,000 pre-operative patients. In this paper, we propose a new aggregation of two …
University of Isfahan（伊斯法罕大学）；Geneva University Hospital（日内瓦大学医院）
Brain tumor segmentation is highly contributive in diagnosing and treatment planning. The manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologists skill. Automated brain tumor segmentation is of high importance, and does not depend on either inter or intra-observation. The objective of this study is to automate the delineation of brain tumors from the FLAIR, T1 weighted, T2 weighted, and T1 weighted contrast-enhanced MR sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof…
Reutlingen University（罗伊特林根大学）；Menoufia University（梅努菲亚大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, De…
Magnetic Resonance (MR) images of different modalities can provide complementary information for clinical diagnosis, but whole modalities are often costly to access. Most existing methods only focus on synthesizing missing images between two modalities, which limits their robustness and efficiency when multiple modalities are missing. To address this problem, we propose a multi-modality generative adversarial network (MGAN) to synthesize three high-quality MR modalities (FLAIR, T1 and T1ce) from one MR modality T2 simultaneously. The experimental results show that the quality of the synthesize…
Kavli Institute for Systems Neuroscience, NTNU（挪威科技大学卡弗里系统神经科学研究所）
Early diagnosis and accurate segmentation of brain tumors are imperative for successful treatment. Unfortunately, manual segmentation is time consuming, costly and despite extensive human expertise often inaccurate. Here, we present an MRI-based tumor segmentation framework using an autoencoder-regularized 3D-convolutional neural network. We trained the model on manually segmented structural T1, T1ce, T2, and Flair MRI images of 335 patients with tumors of variable severity, size and location. We then tested the model using independent data of 125 patients and successfully segmented brain tumo…
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）
Multi-modal magnetic resonance (MR) imaging provides great potential for diagnosing and analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and FLAIR can be obtained simultaneously in a single scanning process. However, acquiring contrast enhanced modalities such as T1ce requires additional time, cost, and injection of contrast agent. As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e.g., brain tumor segmentation) for performance enhancing. In this work, …
The segmentation of diseases is a popular topic explored by researchers in the field of machine learning. Brain tumors are extremely dangerous and require the utmost precision to segment for a successful surgery. Patients with tumors usually take 4 MRI scans, T1, T1gd, T2, and FLAIR, which are then sent to radiologists to segment and analyze for possible future surgery. To create a second segmentation, it would be beneficial to both radiologists and patients in being more confident in their conclusions. We propose using a method performed by radiologists called image segmentation and applying …
University of Michigan（密歇根大学）；Izmir University of Economics（伊兹密尔经济大学）；Ege University School of Medicine（埃格大学医学院）
Purpose; The purpose of this study is to classify glial tumors into grade II, III and IV categories noninvasively by application of machine learning to multi-modal MRI features in comparison with volumetric analysis. Methods; We retrospectively studied 57 glioma patients with pre and postcontrast T1 weighted, T2 weighted, FLAIR images, and ADC maps acquired on a 3T MRI. The tumors were segmented into enhancing and nonenhancing portions, tumor necrosis, cyst and edema using semiautomated segmentation of ITK-SNAP open source tool. We measured total tumor volume, enhancing-nonenhancing tumor, ede…
Deformable image registration is a key task in medical image analysis. The Brain Tumor Sequence Registration challenge (BraTS-Reg) aims at establishing correspondences between pre-operative and follow-up scans of the same patient diagnosed with an adult brain diffuse high-grade glioma and intends to address the challenging task of registering longitudinal data with major tissue appearance changes. In this work, we proposed a two-stage cascaded network based on the Inception and TransMorph models. The dataset for each patient was comprised of a native pre-contrast (T1), a contrast-enhanced T1-w…
Brain tumor imaging has been part of the clinical routine for many years to perform non-invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing primary brain tumors because it allows a volumetric analysis to have a longitudinal follow-up of tumor growth or shrinkage to monitor disease progression and therapy response. In addition, it facilitates further quantitative analysis such as radiomics. Deep learning models, in particular CNNs, have been a methodology of choice in many applications of medical image analysis including brain tumor segmentation. In this …
Mayo Clinic（梅奥诊所）；Moffitt Cancer Center and Research Institute（莫菲特癌症中心与研究所）
Whole brain extraction, also known as skull stripping, is a process in neuroimaging in which non-brain tissue such as skull, eyeballs, skin, etc. are removed from neuroimages. Skull striping is a preliminary step in presurgical planning, cortical reconstruction, and automatic tumor segmentation. Despite a plethora of skull stripping approaches in the literature, few are sufficiently accurate for processing pathology-presenting MRIs, especially MRIs with brain tumors. In this work we propose a deep learning approach for skull striping common MRI sequences in oncology such as T1-weighted with ga…
Inter-scanner and inter-protocol discrepancy in MRI datasets are known to lead to significant quantification variability. Hence image-to-image or scanner-to-scanner translation is a crucial frontier in the area of medical image analysis with a lot of potential applications. Nonetheless, a significant percentage of existing algorithms cannot explicitly exploit and preserve texture details from target scanners and offers individual solutions towards specialized task-specific architectures. In this paper, we design a multi-scale texture transfer to enrich the reconstruction images with more detai…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
University of Zurich（苏黎世大学）；Technical University Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；Mayo Clinic（梅奥诊所）；Helmholtz AI, Helmholtz Munich（亥姆霍兹慕尼黑亥姆霍兹人工智能研究所）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学转化肿瘤研究中心TranslaTUM）；Children’s National Hospital（全国儿童医院）；George Washington University（乔治华盛顿大学）；Finnish Center for Artificial Intelligence（芬兰人工智能中心）；Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School（哈佛医学院阿西努拉·A·马蒂诺斯生物医学成像中心）；Crestview Radiology（克雷斯特维尤放射科）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Yale University（耶鲁大学）；Children’s Hospital of Philadelphia, University of Pennsylvania（宾夕法尼亚大学费城儿童医院）；Duke University Medical Center（杜克大学医学中心）
Glioblastoma, an aggressive brain cancer, is amongst the most lethal of all cancers. Expression of the O6-methylguanine-DNA-methyltransferase (MGMT) gene in glioblastoma tumor tissue is of clinical importance as it has a significant effect on the efficacy of Temozolomide, the primary chemotherapy treatment administered to glioblastoma patients. Currently, MGMT methylation is determined through an invasive brain biopsy and subsequent genetic analysis of the extracted tumor tissue. In this work, we present novel Bayesian classifiers that make probabilistic predictions of MGMT methylation status …
Case Western Reserve University（凯斯西储大学）；Cleveland Clinic（克利夫兰诊所）
A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging following conventional treatment. Previous studies have linked tumor lobar presence and laterality to GBM outcomes, suggesting that disease etiology and progression in GBM may be impacted by tumor location. Hence, in this feasibility study, we seek to investigate the following question: Can tumor location on treatment-naïve MRI provide early cues regarding likelihood of a patient developing pseudo-progression versus tumor…
Purpose: The purpose of this study is to investigate the robustness of a commonly-used convolutional neural network for image segmentation with respect to visually-subtle adversarial perturbations, and suggest new methods to make these networks more robust to such perturbations. Materials and Methods: In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas. A three-dimensional UNet model was implemented to segment four different MR series (T1-weighted, post-contrast T1-weighted, T2- weighted, and T2-weighted FLAIR) into fou…
The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；The Johns Hopkins University（约翰斯·霍普金斯大学）；Kennedy-Krieger Institute（肯尼迪-克里格研究所）
Radiomics is an exciting new area of texture research for extracting quantitative and morphological characteristics of pathological tissue. However, to date, only single images have been used for texture analysis. We have extended radiomic texture methods to use multiparametric (mp) data to get more complete information from all the images. These mpRadiomic methods could potentially provide a platform for stratification of tumor grade as well as assessment of treatment response in brain tumors. In brain, multiparametric MRI (mpMRI) are based on contrast enhanced T1-weighted imaging (T1WI), T2W…
Deep Learning is a promising approach to either automate or simplify several tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based on Adversarial Networks. In particular, we extend SegAN, successfully applied to the same task in a previous work, in two respects: (i) we used a different model input and (ii) we employed a modified loss function to train the model. We tested our approach on two large datasets, made available by the Brain Tumor Image Segmentation Benchmark (BraTS). First, we trained a…
Henry Ford Health System（亨利福特医疗系统）；Wayne State University（韦恩州立大学）
Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. Physicians use MR images as a key tool in the diagnosis and treatment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigates the use of varying amounts of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) MR images created by a generative adversarial network to overcome the lack of annotated medical image data in training separate 2D U-Nets t…
Towards a deep learning approach for classifying treatment response in glioblastomas
Institute for Systems and Robotics - Lisbon and Department of Bioengineering of Instituto Superior Técnico（系统与机器人研究所 - 里斯本及 Instituto Superior Técnico 生物工程系）；Hospital da Luz Learning Health（Luz医院学习健康中心）；Imaging Department, Hospital da Luz（Luz医院影像部门）
Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
Institute of Engineering, Thapathali Campus, Nepal（尼泊尔工程学院，塔帕塔利校区）；Gandaki College of Engineering and Science, Pokhara University, Pokhara, Nepal（加德基工程与科学学院，博克拉大学，博克拉，尼泊尔）；Nepal Engineering College, Changunarayan-4, Bhaktapur, Nepal（尼泊尔工程学院，恰纳拉亚纳-4，巴赫塔普尔，尼泊尔）；Madan Bhandari University of Science and Technology, Chitlang, Nepal（马丹·班达里科学与技术大学，奇塔朗，尼泊尔）；Montreal Neurological Institute, McGill University, Montreal, QC, Canada（蒙特利尔神经研究所，麦吉尔大学，蒙特利尔，魁北克，加拿大）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）；Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria（医学人工智能实验室（MAI实验室），拉各斯，尼日利亚）；Department of Physics, Federal University of Technology, Minna, Nigeria（物理系，联邦技术大学，米纳，尼日利亚）；Department of Computer Science, Johns Hopkins University（计算机科学系，约翰霍普金斯大学）；Department of Radiology and Radiological Science, Johns Hopkins School of Medicine（放射学与放射科学系，约翰霍普金斯医学院）；Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Nepal（尼泊尔应用数学与信息学研究所（NAAMII），尼泊尔）；Institute of Engineering, Purbanchal Campus, Nepal（尼泊尔工程学院，普尔班恰尔校区）
TranslaTUM - Central Institute for Translational Cancer Research, TUM（慕尼黑工业大学TranslaTUM转化癌症研究中央研究所）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院博德研究所）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Klinikum Rechts der Isar, TUM（慕尼黑工业大学 rechts der Isar 医院）；Fraunhofer MEVIS（弗劳恩霍夫医学图像计算研究所）
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could compl…
Massachusetts General Hospital（马萨诸塞总医院）；Harvard Medical School（哈佛医学院）
Multimodal MRI provides complementary and clinically relevant information to probe tissue condition and to characterize various diseases. However, it is often difficult to acquire sufficiently many modalities from the same subject due to limitations in study plans, while quantitative analysis is still demanded. In this work, we propose a unified conditional disentanglement framework to synthesize any arbitrary modality from an input modality. Our framework hinges on a cycle-constrained conditional adversarial training approach, where it can extract a modality-invariant anatomical feature with …
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；North Carolina State University（北卡罗来纳州立大学）
Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) behavior during multi-parametric MRI (mp-MRI) based glioma segmentation as a method to enhance deep learning explainability. Methods: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we designed a novel deep learning model, neural ODE, in which deep feature extraction was governed by an ODE without explicit expression. The dynamics of 1) MR images after interactions with DNN and 2) segmentation formation can be visualized after …
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Duke University Medical Center（杜克大学医学中心）；Indiana University School of Medicine（印第安纳大学医学院）；Indiana University（印第安纳大学）；Thomas Jefferson University（托马斯杰斐逊大学）；University of California San Francisco（加利福尼亚大学旧金山分校）；Yale University（耶鲁大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Missouri（密苏里大学）；Children’s Hospital of Philadelphia, University of Pennsylvania（宾夕法尼亚大学费城儿童医院）；University of Pennsylvania（宾夕法尼亚大学）；Helmholtz Munich（亥姆霍兹慕尼黑）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；University of California San Francisco (UCSF)（加利福尼亚大学旧金山分校）
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；The University of Texas MD Anderson Graduate School of Biomedical Science（德克萨斯大学MD安德森生物医学研究生院）
Hôpital Erasme（埃拉斯姆医院）；Université libre de Bruxelles（布鲁塞尔自由大学）；Center for Microscopy and Molecular Imaging（显微与分子成像中心）
Diffuse gliomas are highly infiltrative tumors whose early diagnosis and follow-up usually rely on magnetic resonance imaging (MRI). However, the limited sensitivity of this technique makes it impossible to directly assess the extent of the glioma cell invasion, leading to sub-optimal treatment planing. Reaction-diffusion growth models have been proposed for decades to extrapolate glioma cell infiltration beyond margins visible on MRI and predict its spatial-temporal evolution. These models nevertheless require an initial condition, that is the tumor cell density values at every location of th…
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
University of Washington（华盛顿大学）；Paul G. Allen School of Computer Science, University of Washington（华盛顿大学保罗·G·艾伦计算机科学学院）；Department of Neurological Surgery, University of Washington（华盛顿大学神经外科（注：此处按要求提取到学院/大学级别，实际原文该机构属于华盛顿大学，故提取为华盛顿大学））
Convolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel 3D multi-path DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multi-modal pre-operative MR images. We hypothesized that the multi-path architecture could achieve more accurate segmentation than a single-path architecture. 258 GBM patients were included in this study. Each patient had four MR images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR) and the manually segmented tumor contour. We built a 3D multi…
Background: Distinction of IDH mutant and wildtype GBMs is challenging on MRI, since conventional imaging shows considerable overlap. While few studies employed deep-learning in a mixed low/high grade glioma population, a GBM-specific model is still lacking in the literature. Our objective was to develop a deep-learning model for IDH prediction in GBM by using Convoluted Neural Networks (CNN) on multiparametric MRI. Methods: We included 100 adult patients with pathologically proven GBM and IDH testing. MRI data included: morphologic sequences, rCBV and ADC maps. Tumor area was obtained by a bo…
Universidade Federal do Espírito Santo（圣埃斯皮里图联邦大学）；Instituto Federal do Espírito Santo（圣埃斯皮里图联邦理工学院）；University of Bern（伯尔尼大学）
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor. As the interest for automatizing brain volume MRI analysis increases, it becomes convenient to have each sequence well identified. However, the unstandardized naming of MRI sequences makes their identification difficult for automated systems, as well as makes it difficult for researches to generate or use datasets for machine learning research. In the face of that, we propose a system for identifying types of brain MRI sequences based on deep learning. By train…
