Deep Probability Contour Framework for Tumour Segmentation and Dose Painting in PET Images

Zhang, W. and Ray, S. (2023) Deep Probability Contour Framework for Tumour Segmentation and Dose Painting in PET Images. In: 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Vancouver, Canada, 8-12 Oct 2023, pp. 534-543. ISBN 9783031439001 (doi: 10.1007/978-3-031-43901-8_51)

Abstract

The use of functional imaging such as PET in radiotherapy (RT) is rapidly expanding with new cancer treatment techniques. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, recent tumour control techniques such as intensity modulated radiation therapy (IMRT) dose painting requires the accurate calculation of multiple nested contours of intensity values to optimise dose distribution across the tumour. Recently, convolutional neural networks (CNNs) have achieved tremendous success in image segmentation tasks, most of which present the output map at a pixel-wise level. However, its ability to accurately recognize precise object boundaries is limited by the loss of information in the successive downsampling layers. In addition, for the dose painting strategy, there is a need to develop image segmentation approaches that reproducibly and accurately identify the high recurrent-risk contours. To address these issues, we propose a novel hybrid-CNN that integrates a kernel smoothing-based probability contour approach (KsPC) to produce contour-based segmentation maps, which mimic expert behaviours and provide accurate probability contours designed to optimise dose painting/IMRT strategies. Instead of user-supplied tuning parameters, our final model, named KsPC-Net, applies a CNN backbone to automatically learn the parameters and leverages the advantage of KsPC to simultaneously identify object boundaries and provide probability contour accordingly. The proposed model demonstrated promising performance in comparison to state-of-the-art models on the MICCAI 2021 challenge dataset (HECKTOR).

• Item Type: Conference Proceedings
• Additional Information: This work was supported by the Carnegie Trust of Scotland PhD Scholarships Fund.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhang, Wenhui and Ray, Professor Surajit
• Authors: Zhang, W., and Ray, S.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Mathematics and Statistics > Statistics
• ISBN: 9783031439001
• Copyright Holders: © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
• First Published: First published in MICCAI 2023, LNCS 14223:534–543
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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