2D and 3D bladder segmentation using U-Net-based deep-learning

Xiangyuan Ma; Lubomir Hadjiiski; Jun Wei; Heang-Ping Chan; Kenny Cha; Richard H. Cohan; Elaine M. Caoili; Ravi Samala; Chuan Zhou; Yao Lu

doi:10.1117/12.2511890

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13 March 2019 2D and 3D bladder segmentation using U-Net-based deep-learning

Xiangyuan Ma, Lubomir Hadjiiski, Jun Wei, Heang-Ping Chan, Kenny Cha, Richard H. Cohan, Elaine M. Caoili, Ravi Samala, Chuan Zhou, Yao Lu

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Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109500Y (2019) https://doi.org/10.1117/12.2511890
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

We are developing a U-Net based deep learning (U-DL) model for bladder segmentation in CT urography (CTU) as a part of a computer-assisted bladder cancer detection and treatment response assessment pipeline. We previously developed a bladder segmentation method that used a deep-learning convolution neural network and level sets (DCNNLS) within a user-input bounding box. The new method does not require a user-input box nor the level sets for postprocessing. To identify the best model for this task, we compared a number of U-DL models: 1) 2D CTU slices or 3D volume as input, 2) different image resolutions, and 3) preprocessing with and without automated cropping on each slice. We evaluated the segmentation performance of the different U-DL models using 3D hand-segmented contours as reference standard. The segmentation accuracy was quantified by the average volume intersection ratio (AVI), average percent volume error (AVE), average absolute volume error (AAVE), average minimum distance (AMD), and the Jaccard index (JI) for a data set of 81 training/validation and 92 independent test cases. For the test set, the best 2D UDL model achieved AVI, AVE, AAVE, AMD, and JI values of 93.4±9.5%, -4.2±14.2%, 9.2±11.5%, 2.7±2.5 mm, 85.0±11.3%, respectively, while the best 3D U-DL achieved 90.6±11.9%, -2.3±21.7%, 11.5±18.5%, 3.1±3.2 mm, and 82.6±14.2%, respectively. For comparison, the corresponding values obtained with our previous DCNN-LS method were 81.9±12.1%, 10.2±16.2%, 14.0±13.0%, 3.6±2.0 mm, and 76.2±11.8%, respectively, for the same test set. The UDL model provided highly accurate bladder segmentation and was more automated than the previous approach.

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Xiangyuan Ma, Lubomir Hadjiiski, Jun Wei, Heang-Ping Chan, Kenny Cha, Richard H. Cohan, Elaine M. Caoili, Ravi Samala, Chuan Zhou, and Yao Lu "2D and 3D bladder segmentation using U-Net-based deep-learning ", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500Y (13 March 2019); https://doi.org/10.1117/12.2511890

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