Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local Versus Global Image Context

Abstract

Chronic kidney disease affects one of every ten adults in USA (over 20 million). Computed tomography (CT) is a widely used imaging modality for kidney disease diagnosis and quantification. However, automatic pathological kidney segmentation is still a challenging task due to large variations in contrast phase, scanning range, pathology, and position in the abdomen, etc. Methods based on global image context (e.g., atlas- or regression-based approaches) do not work well. In this work, we propose to combine deep learning and marginal space learning (MSL), both using local context, for robust kidney detection and segmentation. Here, deep learning is exploited to roughly estimate the kidney center. Instead of performing a whole axial slice classification (i.e., whether it contains a kidney), we detect local image patches containing a kidney. The detected patches are aggregated to generate an estimate of the kidney center. Afterwards, we apply MSL to further refine the pose estimate by constraining the position search to a neighborhood around the initial center. The kidney is then segmented using a discriminative active shape model. The proposed method has been trained on 370 CT scans and tested on 78 unseen cases. It achieves a mean segmentation error of 2.6 and 1.7 mm for the left and right kidney, respectively. Furthermore, it eliminates all gross failures (i.e., segmentation is totally off) in a direct application of MSL.