Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease

Scientific Reports
Kanishka SharmaNassir Navab

Abstract

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common inherited disorder of the kidneys. It is characterized by enlargement of the kidneys caused by progressive development of renal cysts, and thus assessment of total kidney volume (TKV) is crucial for studying disease progression in ADPKD. However, automatic segmentation of polycystic kidneys is a challenging task due to severe alteration in the morphology caused by non-uniform cyst formation and presence of adjacent liver cysts. In this study, an automated segmentation method based on deep learning has been proposed for TKV computation on computed tomography (CT) dataset of ADPKD patients exhibiting mild to moderate or severe renal insufficiency. The proposed method has been trained (n = 165) and tested (n = 79) on a wide range of TKV (321.2-14,670.7 mL) achieving an overall mean Dice Similarity Coefficient of 0.86 ± 0.07 (mean ± SD) between automated and manual segmentations from clinical experts and a mean correlation coefficient (ρ) of 0.98 (p < 0.001) for segmented kidney volume measurements in the entire test set. Our method facilitates fast and reproducible measurements of kidney volumes in agreement with manual segmentations from clinical experts.

References

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Citations

Nov 14, 2018·Radiographics : a Review Publication of the Radiological Society of North America, Inc·Rie TachibanaHiroyuki Yoshida
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Methods Mentioned

BETA
feature extraction

Clinical Trials Mentioned

NCT00491517
NCT01223755
NCT01377246

Software Mentioned

R studio
SIRENA
GeForce
ImageJ
ALADIN

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