TY - GEN
T1 - ARTSA, a New Desktop Application for Automated Renal Tubular Segmentation and Analysis
AU - Santo, Briana A.
AU - Patel, Tatsat R.
AU - Yoshida, Teruhiko
AU - Heymann, Jurgen
AU - Tomaszewski, John E.
AU - Rosenberg, Avi Z.
AU - Kopp, Jeffrey B.
AU - Tutino, Vincent M.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Chronic kidney disease (CKD) is a global health concern, with its progression often characterized by pathological changes in renal tubules. Accurate segmentation and quantitative analysis of renal tubules are critical for understanding disease progression and monitoring treatment efficacy in both model studies and routine diagnostics. Here, we present the development and testing of a novel software tool designed for Automated Renal Tubular Segmentation and Analysis (ARTSA). The ARTSA software employs advanced deep learning algorithms and computational image analysis to automate the segmentation and analysis of renal tubules and medulla in histological kidney tissue sections. This innovative approach eliminates the need for labor-intensive manual segmentation and minimizes human bias, thereby enhancing the precision and efficiency of renal tubular analysis. To gauge ARTSA's performance, we carefully tested it on a balanced dataset of kidney tissue images, covering both healthy and diseased states. The software demonstrated exceptional segmentation performance for both renal tubules and nuclei. Furthermore, the ARTSA software enables comprehensive quantitative analysis of segmented tubules, including measures of tubular curvature, brush border loss, and luminal expansion, which are essential pathologies to quantify. In summary, the ARTSA software represents a significant contribution to the field of digital renal pathology by offering a reliable, automated solution for renal tubular segmentation and analysis.
AB - Chronic kidney disease (CKD) is a global health concern, with its progression often characterized by pathological changes in renal tubules. Accurate segmentation and quantitative analysis of renal tubules are critical for understanding disease progression and monitoring treatment efficacy in both model studies and routine diagnostics. Here, we present the development and testing of a novel software tool designed for Automated Renal Tubular Segmentation and Analysis (ARTSA). The ARTSA software employs advanced deep learning algorithms and computational image analysis to automate the segmentation and analysis of renal tubules and medulla in histological kidney tissue sections. This innovative approach eliminates the need for labor-intensive manual segmentation and minimizes human bias, thereby enhancing the precision and efficiency of renal tubular analysis. To gauge ARTSA's performance, we carefully tested it on a balanced dataset of kidney tissue images, covering both healthy and diseased states. The software demonstrated exceptional segmentation performance for both renal tubules and nuclei. Furthermore, the ARTSA software enables comprehensive quantitative analysis of segmented tubules, including measures of tubular curvature, brush border loss, and luminal expansion, which are essential pathologies to quantify. In summary, the ARTSA software represents a significant contribution to the field of digital renal pathology by offering a reliable, automated solution for renal tubular segmentation and analysis.
KW - automated analysis
KW - Chronic kidney disease
KW - deep learning
KW - histology
KW - image processing
KW - nephrology
KW - renal tubular segmentation
KW - software development
UR - http://www.scopus.com/inward/record.url?scp=85182027812&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182027812&partnerID=8YFLogxK
U2 - 10.1109/WNYISPW60588.2023.10349629
DO - 10.1109/WNYISPW60588.2023.10349629
M3 - Conference contribution
AN - SCOPUS:85182027812
T3 - 2023 IEEE Western New York Image and Signal Processing Workshop, WNYISPW 2023
BT - 2023 IEEE Western New York Image and Signal Processing Workshop, WNYISPW 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Western New York Image and Signal Processing Workshop, WNYISPW 2023
Y2 - 3 November 2023
ER -