Neta Blau1, Eyal Klang2, Nahum Kiryati1, Marianne Amitai2, Orith Portnoy2, Arnaldo Mayer3. 1. Department of Electrical Engineering, Tel-Aviv University, Ramat-Aviv, Israel. 2. Diagnostic Imaging Institute, Sheba Medical Center, Affiliated with Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel. 3. Diagnostic Imaging Institute, Sheba Medical Center, Affiliated with Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel. arnmayer@gmail.com.
Abstract
PURPOSE: Simple renal cysts are a common benign finding in abdominal CT scans. However, since they may evolve in time, simple cysts need to be reported. With an ever-growing number of slices per CT scan, cysts are easily overlooked by the overloaded radiologist. In this paper, we address the detection of simple renal cysts as an incidental finding in a real clinical setting. METHODS: We propose a fully automatic framework for renal cyst detection, supported by a robust segmentation of the kidneys performed by a fully convolutional neural network. A combined 3D distance map of the kidneys and surrounding fluids provides initial candidates for cysts. Eventually, a second convolutional neural network classifies the candidates as cysts or non-cyst objects. RESULTS: Performance was evaluated on 52 abdominal CT scans selected at random in a real radiological workflow and containing over 70 cysts annotated by an experienced radiologist. Setting the minimal cyst diameter to 10 mm, the algorithm detected 59/70 cysts (true-positive rate = 84.3%) while producing an average of 1.6 false-positive per case. CONCLUSIONS: The obtained results suggest the proposed framework is a promising approach for the automatic detection of renal cysts as incidental findings of abdominal CT scans.
PURPOSE: Simple renal cysts are a common benign finding in abdominal CT scans. However, since they may evolve in time, simple cysts need to be reported. With an ever-growing number of slices per CT scan, cysts are easily overlooked by the overloaded radiologist. In this paper, we address the detection of simple renal cysts as an incidental finding in a real clinical setting. METHODS: We propose a fully automatic framework for renal cyst detection, supported by a robust segmentation of the kidneys performed by a fully convolutional neural network. A combined 3D distance map of the kidneys and surrounding fluids provides initial candidates for cysts. Eventually, a second convolutional neural network classifies the candidates as cysts or non-cyst objects. RESULTS: Performance was evaluated on 52 abdominal CT scans selected at random in a real radiological workflow and containing over 70 cysts annotated by an experienced radiologist. Setting the minimal cyst diameter to 10 mm, the algorithm detected 59/70 cysts (true-positive rate = 84.3%) while producing an average of 1.6 false-positive per case. CONCLUSIONS: The obtained results suggest the proposed framework is a promising approach for the automatic detection of renal cysts as incidental findings of abdominal CT scans.
Authors: Jianfei Liu; Shijun Wang; Marius George Linguraru; Jianhua Yao; Ronald M Summers Journal: Med Image Anal Date: 2014-08-15 Impact factor: 8.545
Authors: Holger R Roth; Le Lu; Ari Seff; Kevin M Cherry; Joanne Hoffman; Shijun Wang; Jiamin Liu; Evrim Turkbey; Ronald M Summers Journal: Med Image Comput Comput Assist Interv Date: 2014