Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder-decoder architecture. Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.
Multimodal brain tumor segmentation challenge (BraTS) brings together researchers to improve automated methods for 3D MRI brain tumor segmentation. Tumor segmentation is one of the fundamental vision tasks necessary for diagnosis and treatment planning of the disease. Previous years winning methods were all deep-learning based, thanks to the advent of modern GPUs, which allow fast optimization of deep convolutional neural network architectures. In this work, we explore best practices of 3D semantic segmentation, including conventional encoder-decoder architecture, as well combined loss functio…
Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning. A large dataset size of BraTS 2021 and the advent of modern GPUs provide a better opportunity for deep-learning based approaches to learn tumor representation from the data. In this work, we maintained an encoder-decoder based segmentation network, but focused on a modification of network training process that minimizes redundancy under perturb…
We propose a 2D Encoder-Decoder based deep learning architecture for semantic segmentation, that incorporates anatomical priors by imitating the encoder component of an autoencoder in latent space. The autoencoder is additionally enhanced by means of hierarchical features, extracted by an U-Net module. Our suggested architecture is trained in an end-to-end manner and is evaluated on the example of pelvic bone segmentation in MRI. A comparison to the standard U-Net architecture shows promising improvements.
YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI
ESME Research Lab（ESME研究实验室）；Université Paris-Est Créteil（巴黎东部克雷泰伊大学）；LRE EPITA（EPITA LRE实验室）；Institute of Space Technology（空间技术研究所）；Henri Mondor University Hospital（亨利·蒙多大学医院）
University of Minho（米尼奥大学）；CMEMS-UMinho Research Unit, University of Minho（米尼奥大学CMEMS-UMinho研究中心）；Centro Algoritmi, University of Minho（米尼奥大学阿尔戈里蒂米中心）
Convolutional neural networks (CNNs) have been successfully used for brain tumor segmentation, specifically, fully convolutional networks (FCNs). FCNs can segment a set of voxels at once, having a direct spatial correspondence between units in feature maps (FMs) at a given location and the corresponding classified voxels. In convolutional layers, FMs are merged to create new FMs, so, channel combination is crucial. However, not all FMs have the same relevance for a given class. Recently, in classification problems, Squeeze-and-Excitation (SE) blocks have been proposed to re-calibrate FMs as a …
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；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）
Brain tumor segmentation based on multi-modal magnetic resonance imaging (MRI) plays a pivotal role in assisting brain cancer diagnosis, treatment, and postoperative evaluations. Despite the achieved inspiring performance by existing automatic segmentation methods, multi-modal MRI data are still unavailable in real-world clinical applications due to quite a few uncontrollable factors (e.g. different imaging protocols, data corruption, and patient condition limitations), which lead to a large performance drop during practical applications. In this work, we propose a Deeply supervIsed knowledGE …
University of Calcutta（加尔各答大学）；West Bengal University of Technology（西孟加拉技术大学）
Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy. There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images. These detection and segmentation approache…
The brain tumor segmentation on MRI images is a very difficult and important task which is used in surgical and medical planning and assessments. If experts do the segmentation manually with their own medical knowledge, it will be time-consuming. Therefore, researchers propose methods and systems which can do the segmentation automatically and without any interference. In this article, an unsupervised automatic method for brain tumor segmentation on MRI images is presented. In this method, at first in the pre-processing level, the extra parts which are outside the skull and don't have any help…
Beijing-Dublin International College（北京-都柏林国际学院）；University College Dublin（都柏林大学）
This technical report presents a comparative analysis of existing deep learning (DL) based approaches for brain tumor segmentation with missing MRI modalities. Approaches evaluated include the Adversarial Co-training Network (ACN) and a combination of mmGAN and DeepMedic. A more stable and easy-to-use version of mmGAN is also open-sourced at a GitHub repository. Using the BraTS2018 dataset, this work demonstrates that the state-of-the-art ACN performs better especially when T1c is missing. While a simple combination of mmGAN and DeepMedic also shows strong potentials when only one MRI modality…
School of Computer Science, University of Lincoln（林肯大学计算机科学学院）；National Heart & Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）
In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand crafted features. Fully convolutional networks (FCN) forms the machine learned features and texton based features are considered as hand-crafted features. Random forest (RF) is used to classify the MRI image voxels into normal brain tissues and different parts of tumors, i.e. edema, necrosis and enhancing tumor. The method was evaluated on BRATS 2017 challenge dataset. The results show that the proposed method provi…
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…
In this paper, we propose a novel learning based method for automated segmenta-tion of brain tumor in multimodal MRI images. The machine learned features from fully convolutional neural network (FCN) and hand-designed texton fea-tures are used to classify the MRI image voxels. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI train-ing dataset using the FCN. The learned features are then applied to random for-ests to classify each MRI image voxel into normal brain tissues and different parts of tumor. The method was evaluated on BRATS 2013 …
Medical Resonance Imaging or MRI is a medical image processing technique that used radio waves to scan the body. It is a tomographic imaging technique, principally used in the field of radiology. With the advantage of being a painless diagnostic procedure, MRI allows medical personnel to illustrate clear pictures of the anatomy and the physiological processes occurring in the body, thus allowing early detection and treatment of diseases. These images, combined with image processing techniques may be used in the detection of tumors, difficult to identify with the naked eye. This digital assignm…
One of the most important tasks in medical image processing is the brain's whole tumor segmentation. It assists in quicker clinical assessment and early detection of brain tumors, which is crucial for lifesaving treatment procedures of patients. Because, brain tumors often can be malignant or benign, if they are detected at an early stage. A brain tumor is a collection or a mass of abnormal cells in the brain. The human skull encloses the brain very rigidly and any growth inside this restricted place can cause severe health issues. The detection of brain tumors requires careful and intricate a…
University at Buffalo, The State University of New York（纽约州立大学布法罗分校）
Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical technique for early diagnosis. However, rather than having complete four modalities as in BraTS dataset, it is common to have missing modalities in clinical scenarios. We design a brain tumor segmentation algorithm that is robust to the absence of any modality. Our network includes a channel-independent encoding path and a feature-fusion decoding path. We use self-supervised training through channel dropout and also propose a novel domain adaptation method on feature maps to recover the information from the missing ch…
Vingroup Big Data Institute (VinBigdata)（越南 VinBigdata 大数据研究院）；Hanoi University of Science and Technology (HUST)（河内科技大学）；University of Engineering and Technology (UET), VNU（越南国立大学工程技术大学）
Brain tumor segmentation plays an essential role in medical image analysis. In recent studies, deep convolution neural networks (DCNNs) are extremely powerful to tackle tumor segmentation tasks. We propose in this paper a novel training method that enhances the segmentation results by adding an additional classification branch to the network. The whole network was trained end-to-end on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. On the BraTS's validation set, it achieved an average Dice score of 78.43%, 89.99%, and 84.22% respectively for the enhancing tumo…
Segmentation of images holds an important position in the area of image processing. It becomes more important whi le typically dealing with medical images where presurgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process. Segmentation of 3-D tumor structures from magnetic resonance images (MRI) is a very challenging problem due to the variability of tumor geometry and intensity patterns. Level set evolution combining global smoothness with the flexibility of topology changes offers significant advantages over the conventional statistica…
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Oslo University Hospital（奥斯陆大学医院）；Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto Universitario de Tecnologías de la Información y Comunicaciones（信息技术与通信大学学院）
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 …
University College Dublin（都柏林大学学院）；Université Paris-Est Créteil Val de Marne - Université Paris 12（巴黎东-马恩拉瓦莱大学（巴黎第十二大学））；ADAPT SFI Research Centre（ADAPT SFI研究中心）
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net. Apart from providing real-time segmentation of MRI scans, the proposed architecture does not need large amount of data to train the proposed lightweight U-Net. Moreover, no additional data augmentation step is required. The lightweight U-Ne…
Automatic image segmentation becomes very crucial for tumor detection in medical image processing.In general, manual and semi automatic segmentation techniques require more time and knowledge. However these drawbacks had overcome by automatic segmentation still there needs to develop more appropriate techniques for medical image segmentation. Therefore, we proposed hybrid approach based image segmentation using the combined features of region growing and threshold based segmentation techniques. It is followed by pre-processing stage to provide an accurate brain tumor extraction by the help of …
A Unified Attention U-Net Framework for Cross-Modality Tumor Segmentation in MRI and CT
Department of Computer Science, New Mexico State University（计算机科学系，新墨西哥州立大学）；Department of Plant and Environmental Sciences, New Mexico State University（植物与环境科学系，新墨西哥州立大学）
Brain Tumor Classification from MRI Scans via Transfer Learning and Enhanced Feature Representation
Department of CSE, Netrokona University（计算机科学与工程系，Netrokona大学）；Department of CSE, The University of Rajshahi（计算机科学与工程系，拉贾沙希大学）；Department of Information Technology, Allen E. Paulson College of Engineering and Computing, Georgia Southern University（信息技术系，乔治亚南方大学）；Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University（图像与视觉计算中心（CIVC），人工智能学院，人工智能与工程学院，多媒体大学）
This paper presents an efficient architecture for various image filtering algorithms and tumor characterization using Xilinx System Generator (XSG). This architecture offers an alternative through a graphical user interface that combines MATLAB, Simulink and XSG and explores important aspects concerned to hardware implementation. Performance of this architecture implemented in SPARTAN-3E Starter kit (XC3S500E-FG320) exceeds those of similar or greater resources architectures. The proposed architecture reduces the resources available on target device by 50%.
Background: Glioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health. The main method of acquiring brain tumors in the clinic is MRI. Segmentation of brain tumor regions from multi-modal MRI scan images is helpful for treatment inspection, post-diagnosis monitoring, and effect evaluation of patients. However, the common operation in clinical brain tumor segmentation is still manual segmentation, lead to its time-consuming and large performance difference between different operators, a con…
University of Minho（米尼奥大学）；University Hospital Inselspital（因塞尔施皮塔尔大学医院）；University of Bern（伯尔尼大学）
Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into higher grade tumors over time. Patient management and treatment can vary considerably with tumor grade, ranging from tumor resection followed by a combined radio- and chemotherapy to a "wait and see" approach. Hence, tumor grading is important for adequate treatment planning and monitoring. The gold standard for tumor grading relies on histopathological diagnosis of biopsy specimens. However, this procedure is invasive, …
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；University Hospital Augsburg（奥格斯堡大学医院）
Massachusetts General Hospital（麻省总医院）；Harvard University（哈佛大学）；General Electric Healthcare（通用电气医疗集团）；Indian Institute of Science（印度科学学院）
Gliomas are one of the most frequent brain tumors and are classified into high grade and low grade gliomas. The segmentation of various regions such as tumor core, enhancing tumor etc. plays an important role in determining severity and prognosis. Here, we have developed a multi-threshold model based on attention U-Net for identification of various regions of the tumor in magnetic resonance imaging (MRI). We propose a multi-path segmentation and built three separate models for the different regions of interest. The proposed model achieved mean Dice Coefficient of 0.59, 0.72, and 0.61 for enhan…
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）
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer
University of Information Technology（信息科技大学）；Vietnam National University（越南国家大学）；College of Technology and Design, University of Economics Ho Chi Minh City（胡志明市经济大学技术与设计学院）；Ho Chi Minh City Open University（胡志明市开放大学）
Brain tumor segmentation is a challenging problem in medical image analysis. The endpoint is to generate the salient masks that accurately identify brain tumor regions in an fMRI screening. In this paper, we propose a novel attention gate (AG model) for brain tumor segmentation that utilizes both the edge detecting unit and the attention gated network to highlight and segment the salient regions from fMRI images. This feature enables us to eliminate the necessity of having to explicitly point towards the damaged area(external tissue localization) and classify(classification) as per classical c…
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）
Segmenting a MRI images into homogeneous texture regions representing disparate tissue types is often a useful preprocessing step in the computer-assisted detection of breast cancer. That is why we proposed new algorithm to detect cancer in mammogram breast cancer images. In this paper we proposed segmentation using vector quantization technique. Here we used Linde Buzo-Gray algorithm (LBG) for segmentation of MRI images. Initially a codebook of size 128 was generated for MRI images. These code vectors were further clustered in 8 clusters using same LBG algorithm. These 8 images were displayed…
Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. Variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorf…
