Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning

Sun, Li; Zhang, Songtao; Chen, Hang; Luo, Lin

doi:10.3389/fnins.2019.00810

Cited by 186 publications

(125 citation statements)

References 23 publications

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“…There is active research in combining both deep learning features and radiomics features that shows improved results. [72][73][74] Potential clinical aPPlications Radiomics in oncology Radiomics has been widely studied for application in diagnosis and treatment prognosis/selection in oncology, primarily due to the existence of large imaging data sets used for staging, often containing delineations of tumours and organs at risk necessary for radiation treatment planning. These data sets can be used to train diagnostic and prognostic models for a variety of cancer types and sites.…”

Section: Deep Learning For Fully Automated Workflowsmentioning

confidence: 99%

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Radiomics: from qualitative to quantitative imaging

Rogers

Seetha

Lieverse

et al. 2020

The British Journal of Radiology

204

130

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Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

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Section: Deep Learning For Fully Automated Workflowsmentioning

confidence: 99%

“…103 Deep learning can also be used complementary to traditional handcrafted radiomics studies. For example, studies 72,73 focused on using deep networks for segmentation, followed by radiomics analysis for survival prediction.…”

Section: Lungmentioning

confidence: 99%

Radiomics: from qualitative to quantitative imaging

Rogers

Seetha

Lieverse

et al. 2020

The British Journal of Radiology

204

130

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“…The author [18] developed a DLF (Deep Learning Framework) for brain tumor segments and has predicted the survival of glioma by means of MRI. We use 3 separate 3D-CNN architecture sets for robust performance through a tumour segmentation rule in the majority.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning-Based HCNN and CRF-RRNN Model for Brain Tumor Segmentation

et al. 2020

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This paper proposes a strategy where a structure is developed to recognize and order the tumor type. Over a time of years, numerous specialists have been examined and proposed a technique in this space. A brain tumor segmentation approach is developed based on efficient, deep learning techniques implemented in a unified system to achieve the appearance and spatial accuracy outcomes through Conditional Radom Fields (CRF) and Heterogeneous Convolution Neural Networks (HCNN). In these steps the 2D image patching and picture slices of the deep-learning model is developed. The Proposed method has following steps as follows: 1) train HCNN by image patches; 2) train CRF with CRF-Recurrent Regression based Neural Network (RRNN) by means of image slices with fixed variables of HCNN; 3) fine tune with HCNN and CRF-RRNN image slices. In general, 3 segmentation models have been trained using axial-, coronary-and sagittal image patches and slices, Further assembled into brain tumor segments using a voting fusion technique and it can be examined with Internet of Medical Things (IoMT) Platform. The experimental results proved that our approach has been capable of developing a Flair, T1c and T2 segmenting model and of achieving good performance as with Flair, T1, T1c, and T2 scans.

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“…In the third category, hybrid approaches, deep features that are extracted using DL methods, handcrafted features that are extracted from automatic segmented tumor regions, and clinical data are combined to create a feature fusion matrix. This matrix is then used as input to train ML algorithms [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning of multiview CNN models for survival time prediction of brain tumor patients using multimodal MRI scans

A¹,

Çevik²

2021

Turk J Elec Eng & Comp Sci

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Brain tumors have been one of the most common life-threatening diseases for all mankind. There have been huge efforts dedicated to the development of medical imaging techniques and radiomics to diagnose tumor patients quickly and efficiently. One of the main aims is to ensure that pre-operative overall survival time (OS) prediction is accurate. Recently, deep learning (DL) algorithms, and particularly convolutional neural networks (CNNs) achieved promising performances in almost all computer vision fields. CNNs demand large training datasets and high computational costs. However, curating large annotated medical datasets are difficult and resource-intensive. The performances of single learners are also unsatisfactory for small datasets. Thus, this study was conducted to improve the performance of CNN models on small volumetric datasets through developing a DL-based ensemble method for OS classification of brain tumor patients using multi-modal magnetic resonance images (MRI). First, we proposed Multi-View CNNs: OS classifiers based on representing the 3D MRI data as a set of 2D slices along all three planes (axial, sagittal, and coronal) and process them using 2D CNNs. Subsequently, the predicted probabilities by the Multi-View CNN models were fused using standard machine learning algorithms. The proposed approach was experimentally evaluated on 163 patients obtained from the BraTS'17 training dataset. Our best model achieved an AUC and accuracy values of 0.93 and 92.9%, respectively, on classifying patients with brain tumors into two OS groups, outperforming current state-of-the-art results. In addition, the FLAIR MRI modality yielded the best classification accuracy compared to other MRI modalities. Similarly, axial projections had the best classification performance compared to coronal and sagittal projections. Our findings may provide valuable insights for physicians in advancing treatment planning via noninvasive and accurate prediction of survival using only MRIs at the time of diagnosis.

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Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning

Cited by 186 publications

References 23 publications

Radiomics: from qualitative to quantitative imaging

Radiomics: from qualitative to quantitative imaging

Deep Learning-Based HCNN and CRF-RRNN Model for Brain Tumor Segmentation

Ensemble learning of multiview CNN models for survival time prediction of brain tumor patients using multimodal MRI scans

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