Characterisation and Automatic Detection of Lymph Nodes on MR Colorectal Images

Abstract: Colorectal cancer is the second most common cause of death in Western countries. It is often curable by chemoradiotherapy and/or surgery; however, accurate staging has a significant impact on patient management and outcome. Numerous clinical reports attest to the fact that staging is not currently satisfactory, and so more precise methods are required for effective treatment. The three major components of disease staging are tumour size; whether or not there is distal metastatic spread; and the extent of lymph node involvement. Of these, the latter is currently by far the hardest to quantify, and it is the subject of this paper. Lymph nodes are distributed throughout the mesorectal fascia that envelops the colorectum. In practice, they are detected and assessed by clinicians using properties such as their size and shape. We are not aware of any previous image analysis approach for colorectal images that makes this subjective approach more scientific. To aid precise staging and surgery, we have developed methods that characterises lymph nodes by extracting implicit properties as computed from magnetic resonance colorectal images. We first learn the probability density function (PDF) of the intensities of the mesorectal fascia and find that it closely approximates a Gaussian distribution. The parameters of a Gaussian, fitted to the PDF, were estimated and the mean intensity of a lymph node candidate was compared with it. The fitting provides an explicit criterion for a region to be classed as a lymph node: namely, it is an outlier of the Gaussian distribution. As a key part of this process, we need to segment the boundaries of the mesorectal fascia, which is enclosed by two closed contours. Clinicians recognise the outer contour as thin edges. Since the thin edges are often ambiguous and disconnected, differentiating them from neighbouring tissues is a non-trivial problem; the surrounding tissues have no significant difference from the mesorectal fascia in both intensity and texture. We employed a level set method to segment three sets of objects: the mesorectal fascia, the colorectum, and lymph node candidates. Our segmentation results led us to build a PDF and to use it for the criterion that we propose. The whole process of implementation of our methods is automatic including the lookup of lymph candidates. The results of clinical cases are summarised in the paper.