Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients

Juanco-Müller, Ángel Víctor; Mota, João F. C.; Goatman, Keith A; Hoogendoorn, Corné

doi:10.1007/978-3-030-98253-9_24

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The Clinical + DeepFeat + Radiomics combination led to a C-index of 0.786, which was lower than the Deep-Features + Radiomics combination, meaning that we could achieve accurate predictions of patients’ survival days without employing clinical data. Table 11 also demonstrated that the features based on radiomics and DeepFeat achieved the highest C-index scores (0.821) compared to those of methods that were specially designed for the prediction of patients’ survival days, such as [ 38 , 39 , 40 , 41 ].…”

Section: Resultsmentioning

confidence: 97%

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

Mazher

Qayyum

Puig

et al. 2022

Entropy

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To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)—GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model’s encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791±0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients’ survival days.

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Section: Resultsmentioning

confidence: 97%

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

Mazher

Qayyum

Puig

et al. 2022

Entropy

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“…The proposed combined feature solution is always shifted toward the higher values on the right as shown in Figure 11. The SHAP explainability method (SHapley Additive exPlanations) 33 has been used to measure the importance of deep CNN and radiomics features 34,35 . The red color shows the high features and the blue color presents the low feature values.…”

Section: Resultsmentioning

confidence: 99%

“…The SHAP explainability method (SHapley Additive exPlanations) 33 has been used to measure the importance of deep CNN and radiomics features. 34,35 The red color shows the high features and the blue color presents the low feature values.…”

Section: Performance Analysis Of the Proposed Modelmentioning

confidence: 99%

Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

Mazher,

Qayyum,

Puig

et al. 2023

Int J Imaging Syst Tech

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Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state‐of‐the‐art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi‐scale‐aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor‐based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature‐based method achieved the highest performance for survival days prediction over the state‐of‐the‐art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning‐based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days.

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Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Andrearczyk

Oreiller

Boughdad

et al. 2022

Lecture Notes in Computer Science

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Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients

Cited by 3 publications

References 15 publications

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

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