Kernel Smoothing-based Probability Contours for Tumour Segmentation

Zhang, W. and Ray, S. (2022) Kernel Smoothing-based Probability Contours for Tumour Segmentation. 26th UK Conference on Medical Image Understanding and Analysis (MIUA 2022), University of Cambridge, 27-29 July 2022.

Abstract

Statistical imaging together with other machine learning techniques are the epitome of digitalizing healthcare and are culminating towards developing innovative tools for automatic analysis of three-dimensional radiological images — PET (Positron Emission Tomography) images. In this project, we have developed a kernel smoothing probability contour method on PET image segmentation which can be trained with a relatively low number of samples. A clear advantage of our kernel smoothing probability contour method is that it provides a surface over images which produces contour-based results rather than pixel-wise results, thus mimicking human observers’ behaviour. In addition, our methodology provides the tools for developing a probabilistic approach with uncertainty measurement along with the segmentation.

• Item Type: Conference or Workshop Item
• 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 > School of Mathematics and Statistics > Statistics

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