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Article

Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease

1
Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
2
The Rogosin Institute and Department of Medicine Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
3
Departments of Radiology at Weill Cornell Medicine and Biomedical Engineering, Cornell University, New York, NY 10065, USA
4
Columbia College of Physicians and Surgeons, Cornell University, New York, NY 10027, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Dong-Hyun Kim
Tomography 2022, 8(4), 1804-1819; https://doi.org/10.3390/tomography8040152
Received: 21 May 2022 / Revised: 1 July 2022 / Accepted: 8 July 2022 / Published: 13 July 2022
(This article belongs to the Section Artificial Intelligence in Medical Imaging)
Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability. View Full-Text
Keywords: liver volume; kidney volume; spleen volume; ADPKD; artificial intelligence; interobserver variability; machine learning liver volume; kidney volume; spleen volume; ADPKD; artificial intelligence; interobserver variability; machine learning
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MDPI and ACS Style

Sharbatdaran, A.; Romano, D.; Teichman, K.; Dev, H.; Raza, S.I.; Goel, A.; Moghadam, M.C.; Blumenfeld, J.D.; Chevalier, J.M.; Shimonov, D.; Shih, G.; Wang, Y.; Prince, M.R. Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease. Tomography 2022, 8, 1804-1819. https://doi.org/10.3390/tomography8040152

AMA Style

Sharbatdaran A, Romano D, Teichman K, Dev H, Raza SI, Goel A, Moghadam MC, Blumenfeld JD, Chevalier JM, Shimonov D, Shih G, Wang Y, Prince MR. Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease. Tomography. 2022; 8(4):1804-1819. https://doi.org/10.3390/tomography8040152

Chicago/Turabian Style

Sharbatdaran, Arman, Dominick Romano, Kurt Teichman, Hreedi Dev, Syed I. Raza, Akshay Goel, Mina C. Moghadam, Jon D. Blumenfeld, James M. Chevalier, Daniil Shimonov, George Shih, Yi Wang, and Martin R. Prince. 2022. "Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease" Tomography 8, no. 4: 1804-1819. https://doi.org/10.3390/tomography8040152

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