ISSN: 1899-0967
Polish Journal of Radiology
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vol. 88
Head and neck radiology
Original paper

Fully automated clinical target volume segmentation for glioblastoma radiotherapy using a deep convolutional neural network

Sogand Sadeghi
Mostafa Farzin
Somayeh Gholami

Department of Nuclear Physics, Faculty of Sciences, University of Mazandaran, Babolsar, Iran
Brain and Spinal Cord Injury Research Centre, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
Department of Radiation Oncology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
Pol J Radiol 2023; 88: e31-e40
Online publish date: 2023/01/19
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Target volume delineation is a crucial step prior to radiotherapy planning in radiotherapy for glioblastoma. This step is performed manually, which is time-consuming and prone to intra- and inter-rater variabilities. Therefore, the purpose of this study is to evaluate a deep convolutional neural network (CNN) model for automatic segmentation of clinical target volume (CTV) in glioblastoma patients.

Material and methods:
In this study, the modified Segmentation-Net (SegNet) model with deep supervision and residual-based skip connection mechanism was trained on 259 glioblastoma patients from the Multimodal Brain Tumour Image Segmentation Benchmark (BraTS) 2019 Challenge dataset for segmentation of gross tumour volume (GTV). Then, the pre-trained CNN model was fine-tuned with an independent clinical dataset (n = 37) to perform the CTV segmentation. In the process of fine-tuning, to generate a CT segmentation mask, both CT and MRI scans were simultaneously used as input data. The performance of the CNN model in terms of segmentation accuracy was evaluated on an independent clinical test dataset (n = 15) using the Dice Similarity Coefficient (DSC) and Hausdorff distance. The impact of auto-segmented CTV definition on dosimetry was also analysed.

The proposed model achieved the segmentation results with a DSC of 89.60 ± 3.56% and Hausdorff distance of 1.49 ± 0.65 mm. A statistically significant difference was found for the Dmin and Dmax of the CTV between manually and automatically planned doses.

The results of our study suggest that our CNN-based auto-contouring system can be used for segmentation of CTVs to facilitate the brain tumour radiotherapy workflow.


deep learning, convolutional neural networks, brain tumour segmentation, clinical target volume, treatment planning

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