An Integrative Analysis of Image Segmentation and Survival of Brain Tumour Patients

Starke, Sebastian; Eckert, Carlchristian; Zwanenburg, Alex; Speidel, Stefanie; Löck, Steffen; Leger, Stefan

doi:10.1007/978-3-030-46640-4_35

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…For the performance evaluation of the segmentation model, four metrics named DSC, sensitivity, specificity, and the HD are used. 7 It can be seen that this study has outperformed the existing techniques 10,16,17,30,34,48 for brain tumor segmentation. By analyzing the BraTS2019 and BraTS2020 datasets, the BraTS2020 dataset outperformed due to its larger training set.…”

Section: Segmentation Resultsmentioning

confidence: 74%

“…It can be seen that this study has outperformed the existing techniques 10,16,17,30,34,48 for brain tumor segmentation. By analyzing the BraTS2019 and BraTS2020 datasets, the BraTS2020 dataset outperformed due to its larger training set.…”

Section: Resultsmentioning

confidence: 76%

“…To compare the effect of different dilation rates on tumor segmentation, the MLDCNN model was trained separately for five MLDC blocks. The implemented models were compared with the existing studies 10,16,17,30,34,48 in Table 2. For the performance evaluation of the segmentation model, four metrics named DSC, sensitivity, specificity, and the HD are used 7 …”

Section: Resultsmentioning

confidence: 99%

“…2D‐CNN‐based models split the 3D volumes into 2D slices and perform 2D operations on these slices. Starke et al 30 proposed an ensemble of 2D‐UNET‐based models for axial, sagittal, and coronal views. The output of five training models was ensembled using majority voting for validation.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Brain tumor segmentation and multiview multiscale‐based radiomic model for patient's overall survival prediction

Fiaz

Madni

Anwar

et al. 2021

Int J Imaging Syst Tech

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A brain tumor is the most common primary brain malignancy. Delaying in brain tumor diagnosis is a primary cause of death in affected individuals. Therefore, early diagnosis of a brain tumor is essential for treatment planning and prognosis. In this study, the multilevel dilated convolutional neural network (MLDCNN) model is used for brain tumor segmentation. MLDCNN model is implemented independently for five MLDC blocks with a different combination of dilation rates to analyze their impact on brain tumor segmentation. For each segmentation model, overall survival time prediction is performed independently. An automated system is proposed for the overall survival time prediction of patients suffering from a brain tumor. First, shape and multiscale texture‐based features are extracted from LoG filtered and wavelet decomposed images of the magnetic resonance imaging scans. The proposed model utilizes 3D information by extracting radiomic features from axial, coronal, and sagittal views. These features are reduced using an extra tree classifier to avoid overfitting. Random forest algorithms are applied on selected feature sets to predict overall survival time in days. Extensive experimentation is performed for the segmentation and survival time prediction on the publicly available BraTS2019 and BraTS 2020 datasets. Results demonstrate that the proposed approach achieved the least mean squared error value in the survival time prediction task.

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Section: Segmentation Resultsmentioning

confidence: 74%

Section: Resultsmentioning

confidence: 76%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Brain tumor segmentation and multiview multiscale‐based radiomic model for patient's overall survival prediction

Fiaz

Madni

Anwar

et al. 2021

Int J Imaging Syst Tech

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“…A 2D U-Net-based architecture was used with symmetric decoding blocks with skipconnections from encoding block. 70 Authors have used an ensemble of five trained models for prediction of F I G U R E 4 Correlation matrix of the selected features computed from the MRI scans. Highly correlated features are represented by green colour, while red colour shows close to zero (r 2 ) validation data.…”

Section: Evaluation Of Segmentation Modelmentioning

confidence: 99%

Multi‐level dilated convolutional neural network for brain tumour segmentation and multi‐view‐based radiomics for overall survival prediction

Rafi

Madni

Janjua

et al. 2021

Int J Imaging Syst Tech

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Glioblastoma (GBM) is the most high-risk and grievous tumour in the brain that causes the death of more than 50% of the patients within one to 2 years after diagnosis. Accurate detection and prognosis of this disease are critical to provide essential guidelines for treatment planning. This study proposed using a deep learning-based network for the GBM segmentation and radiomic features for the patient's overall survival (OS) time prediction. The segmentation model used in this study was a modified U-Net-based deep 3D multi-level dilated convolutional neural network. It uses multiple kernels of altered sizes to capture contextual information at different levels. The proposed scheme for OS time prediction overcomes the problem of information loss caused by the derivation of features in a single view due to the variation in the neighbouring pixels of the tumorous region. The selected features were based on texture, shape, and volume. These features were computed from the segmented components of tumour in axial, coronal, and sagittal views of magnetic resonance imaging slices. The proposed models were trained and evaluated on the BraTS 2019 dataset. Experimental results of OS time prediction on the validation data have showed an accuracy of 48.3%, with the mean squared error of 92 599.598. On the validation data, the segmentation model achieved a mean dice similarity coefficient of 0.75, 0.89, and 0.80 for enhancing tumour, whole tumour, and tumour core, respectively. Future work is warranted to improve the overall performance of OS time prediction based on the findings in this study.

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3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction

Agravat

Raval

2021

Lecture Notes in Computer Science

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An Integrative Analysis of Image Segmentation and Survival of Brain Tumour Patients

Cited by 8 publications

References 25 publications

Brain tumor segmentation and multiview multiscale‐based radiomic model for patient's overall survival prediction

Brain tumor segmentation and multiview multiscale‐based radiomic model for patient's overall survival prediction

Multi‐level dilated convolutional neural network for brain tumour segmentation and multi‐view‐based radiomics for overall survival prediction

3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction

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