KEVS: Enhancing Segmentation of Visceral Adipose Tissue in Pre-Cystectomy CT with Gaussian Kernel Density Estimation
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
Purpose: The distribution of visceral adipose tissue (VAT) in cystectomy
patients is indicative of the incidence of post-operative complications.
Existing VAT segmentation methods for computed tomography (CT) employing
intensity thresholding have limitations relating to inter-observer variability.
Moreover, the difficulty in creating ground-truth masks limits the development
of deep learning (DL) models for this task. This paper introduces a novel
method for VAT prediction in pre-cystectomy CT, which is fully automated and
does not require ground-truth VAT masks for training, overcoming aforementioned
limitations. Methods: We introduce the Kernel density Enhanced VAT Segmentator
( KEVS), combining a DL semantic segmentation model, for multi-body feature
prediction, with Gaussian kernel density estimation analysis of predicted
subcutaneous adipose tissue to achieve accurate scan-specific predictions of
VAT in the abdominal cavity. Uniquely for a DL pipeline, KEVS does not require
ground-truth VAT masks. Results: We verify the ability of KEVS to accurately
segment abdominal organs in unseen CT data and compare KEVS VAT segmentation
predictions to existing state-of-the-art (SOTA) approaches in a dataset of 20
pre-cystectomy CT scans, collected from University College London Hospital
(UCLH-Cyst), with expert ground-truth annotations. KEVS presents a 4.80% and
6.02% improvement in Dice Coefficient over the second best DL and
thresholding-based VAT segmentation techniques respectively when evaluated on
UCLH-Cyst. Conclusion: This research introduces KEVS; an automated, SOTA method
for the prediction of VAT in pre-cystectomy CT which eliminates inter-observer
variability and is trained entirely on open-source CT datasets which do not
contain ground-truth VAT masks.
