From coarse to fine: a deep 3D probability volume contour framework for tumor segmentation and dose painting in PET images

Zhang, W. and Ray, S. (2023) From coarse to fine: a deep 3D probability volume contour framework for tumor segmentation and dose painting in PET images. Frontiers In Radiology, 3, 1225215. (doi: 10.3389/fradi.2023.1225215)

Abstract

With the increasing integration of functional imaging techniques like Positron Emission Tomography (PET) into radiotherapy (RT) practices, a paradigm shift in cancer treatment methodologies is underway. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, novel tumour control methods, such as intensity modulated radiation therapy (IMRT) dose painting, demand the precise delineation of multiple intensity value contours to ensure optimal tumour dose distribution. Recently, convolutional neural networks (CNNs) have made significant strides in 3D image segmentation tasks, most of which present the output map at a voxel-wise level. However, because of information loss in subsequent downsampling layers, they frequently fail to precisely identify precise object boundaries. Moreover, in the context of dose painting strategies, there is an imperative need for reliable and precise image segmentation techniques to delineate high recurrence-risk contours.To address these challenges, we introduce a 3D coarse-to-fine framework, integrating a CNN with a kernel smoothing-based probability volume contour approach (KsPC). This integrated approach generates contour-based segmentation volumes, mimicking expert-level precision and providing accurate probability contours crucial for optimizing dose painting/IMRT strategies. Our final model, named KsPC-Net, leverages a CNN backbone to automatically learn parameters in the kernel smoothing process, thereby obviating the need for user-supplied tuning parameters.The 3D KsPC-Net exploits the strength of KsPC to simultaneously identify object boundaries and generate corresponding probability volume contours, which can be trained within an endto-end framework. The proposed model has demonstrated promising performance, surpassing state-of-the-art models when tested against the MICCAI 2021 challenge dataset (HECKTOR).

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhang, Miss Wenhui and Ray, Professor Surajit
• Authors: Zhang, W., and Ray, S.
• College/School: College of Science and Engineering > School of Mathematics and Statistics; College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Frontiers In Radiology
• Publisher: Frontiers Media
• ISSN: 2673-8740
• ISSN (Online): 2673-8740
• Copyright Holders: Copyright © 2023 Zhang and Ray
• First Published: First published in Frontiers in Radiology 3:1225215
• Publisher Policy: Reproduced under a Creative Commons licence

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
309368		Usage style analytics for BT problem.	Muffy Calder	Engineering and Physical Sciences Research Council (EPSRC)	EP/R511705/1	S&E - College Senior Management