AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation

Coupé, Pierrick; Mansencal, Boris; Clément, Michel; Giraud, Rémi; Senneville, Baudouin Denis de; Ta, Vinh-Thong; Lepetit, Vincent; Manjón, José V.

doi:10.1016/j.neuroimage.2020.117026

Cited by 111 publications

(112 citation statements)

References 48 publications

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“…Moreover, they are also required for exploring the efficiency of using CNN in smart and embedded systems. • In the CNN context, ensemble learning [342,343] represents a prospective research area. The collection of different and multiple architectures will support the model in improving its generalizability across different image categories through extracting several levels of semantic image representation.…”

Section: Discussionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Section: Discussionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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“…The major drawback of convolutional neural network models (CNN) lies in the fuzzy segmentation outcomes and the spatial information reduction caused by the strides of convolutions and pooling operations 32 . To further improve the segmentation accuracy and efficiency, several advanced strategies have been applied to obtain better segmentation results 21 , 25 , 33 , 34 with approaches like dilated convolution/pooling 35 – 37 , skip connections 38 , 39 , as well as additional analysis and new post-processing modules like Conditional Random Field (CRF) and Hidden Conditional Random Field (HCRF) 10 , 40 , 41 . Using the dilated convolution method causes a large receptive field to be used without applying the pooling layer to the aim of relieving the issue of information loss during the training phase.…”

Section: Methodsmentioning

confidence: 99%

“…An AssemblyNet model was proposed by Coupé et al . 25 which uses the parliamentary decision-making concept for 3D whole-brain MRI segmentation. This parliamentary network is able to solve unseen problems, take complex decisions, and reach a relevant consensus.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Ranjbarzadeh

Kasgari

Ghoushchi

et al. 2021

Sci Rep

382

129

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Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

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“…Recently, deep learning has been extensively applied in medical image segmentation; for example, segmenting local lesions such as tumors [ 14 , 15 , 16 ] and organs such as brain tissues [ 5 , 17 , 18 ]. By pooling features with different resolutions in the encoding path and recovering sharp object boundaries in the decoding path, the U-Net [ 19 ] can capture rich contextual information because of this encoding-decoding manner.…”

Section: Introductionmentioning

confidence: 99%

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

Long

Song

et al. 2021

Sensors

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Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations. Therefore, we propose a novel network by introducing spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework, which is helpful in extracting rich multi-scale features while highlighting the details of higher-level features in the encoding part, and recovering the corresponding localization to a higher resolution layer in the decoding part. Concretely, we propose two information extractors, multi-branch pooling, called MP, in the encoding part, and multi-branch dense prediction, called MDP, in the decoding part, to extract multi-scale features. Additionally, we designed a new multi-branch output structure with MDP in the decoding part to form more accurate edge-preserving predicting maps by integrating the dense adjacent prediction features at different scales. Finally, the proposed method is tested on datasets MRbrainS13, IBSR18, and ISeg2017. We find that the proposed network performs higher accuracy in segmenting MRI brain tissues and it is better than the leading method of 2018 at the segmentation of GM and CSF. Therefore, it can be a useful tool for diagnostic applications, such as brain MRI segmentation and diagnosing.

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AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation

Cited by 111 publications

References 48 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

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