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

Purpose

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.

Results

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.

Conclusions

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.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm³ (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging

The management and treatment of high-grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi-automatic segmentation method based on diffusion tensor imaging; (ii) two-dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pre-treatment stereotactic biopsy or at surgical resection. Our two-dimensional morphological analysis outperforms previous methods with high cross-validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks-based classifier.