Spine-anchored active contour-based renal spatial prioritization for precision-constrained volumetric segmentation of bilateral nephric tumors via spine-guided 3D deep encoders in abdominopelvic tomographic data.

Abstract

Bilateral renal tumors have proven challenging to precisely segment out of abdominopelvic CT scans. In this regard, the present paper explores a hybridization technique based on spine anchoring using active contour modelling and spatial prioritization for enhanced renal volumetric segmentation. To begin with, the presented model is built upon the spine detection that helps define an anatomically sound anchor for subsequent processing, which involves spatially constrained localization of renal areas using active contour modelling. The pre-processing step can be considered a spatial attention mechanism that limits the search space of the target region and provides additional anatomical context. Next, a 3D-UNet network is utilized for the precise segmentation of nephric tumors with independent coding of left/right kidneys to account for morphological variations. The model's evaluation was performed on the KiTS19 dataset where the Dice coefficient was equal to 90.39%, and Jaccard index was 86.97%. Moreover, kidney segmentation demonstrated even more accurate results (Dice coefficient of 97.53%).