Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests

2015/10/23 by Kanishka Sharma, Sharma, Kanishka, Loïc Peter +13 · 1 citation
Biochemistry, Genetics and Molecular Biology · Medicine · #Genetic and Kidney Cyst Diseases #Renal and related cancers #Fetal and Pediatric Neurological Disorders

paper · pdf · doi:10.48550/arxiv.1510.06915

Abstract

This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data. ADPKD severely alters the shape of the kidneys due to non-uniform formation of cysts. As a consequence, fully automatic segmentation of such kidneys is very challenging. We present a segmentation method with minimal user interaction based on a random forest classifier. One of the major novelties of the proposed approach is the usage of geodesic distance volumes as additional source of information. These volumes contain the intensity weighted distance to a manual outline of the respective kidney in only one slice (for each kidney) of the CT volume. We evaluate our method qualitatively and quantitatively on 55 CT acquisitions using ground truth annotations from clinical experts.

Cited by

• Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network

Related

• Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks
• Robust Kidney Abnormality Segmentation: A Validation Study of an AI-Based Framework
• Automatic Segmentation of the Kidneys and Cystic Renal Lesions on Non-Contrast CT Using a Convolutional Neural Network
• CovHuSeg: An Enhanced Approach for Kidney Pathology Segmentation
• An Ensemble of 2.5D ResUnet Based Models for Segmentation for Kidney and Masses