Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

One of the key criteria that informs patient management decisions for colorectal cancer is the extent of the shortest distance from the edge of the primary tumour to the edge of the mesorectum, also referred to as circumferential resection margin (CRM). This region is resected during surgery. The CRM is difficult for clinicians to measure accurately, particularly from 2D slice data. We present a method for automatically calculating and visualising the CRM distances in colorectal cancer MR images. We use local phase of the monogenic signal calculated from the MR image intensities to find edge and ridge features within the data. A non-parametric mixture model is then used to describe image intensity values within level set framework in order to segment the mesorectal fascia and the corresponding tumour and lymph nodes, as distinct regions. This segmentation is used to provide an automatic analysis of the shortest distance resection margin, and we show that this is consistent with that of the clinically accepted MERCURY method. We use the segmentation to provide a 3D visualisation of where the resection margin is smallest. Finally, we reconstruct a 3D map of the segmented anatomy. Both the visualisation methods provide a useful tool to aid surgeons in their treatment planning.

Original publication




Journal article


Med Image Anal

Publication Date





494 - 509


Algorithms, Artificial Intelligence, Colorectal Neoplasms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity