Computer methods and programs in biomedicine · 2022
KUB-UNet: Segmentation of Organs of Urinary System from a KUB X-ray Image.
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Rani Geeta, Thakkar Priyam, Verma Akshat, Mehta Vanshika, Chavan Rugved, Dhaka Vijaypal Singh, Sharma Ravindra Kumar, Vocaturo Eugenio, Zumpano Ester
Аннотация
PURPOSE: The alarming increase in diseases of urinary system is a cause of concern for the populace and health experts. The traditional techniques used for the diagnosis of these diseases are inconvenient for patients, require high cost, and additional waiting time for generating the reports. The objective of this research is to utilize the proven potential of Artificial Intelligence for organ segmentation. Correct identification and segmentation of the region of interest in a medical image are important to enhance the accuracy of disease diagnosis. Also, it improves the reliability of the system by ensuring the extraction of features only from the region of interest.
METHOD: A lot of research works are proposed in the literature for the segmentation of organs using MRI, CT scans, and ultrasound images. But, the segmentation of kidneys, ureters, and bladder from KUB X-ray images is found under explored. Also, there is a lack of validated datasets comprising KUB X-ray images. These challenges motivated the authors to tie up with the team of radiologists and gather the anonymous and validated dataset that can be used to automate the diagnosis of diseases of the urinary system. Further, they proposed a KUB-UNet model for semantic segmentation of the urinary system.
RESULTS: The proposed KUB-UNet model reported the highest accuracy of 99.18% for segmentation of organs of urinary system.
CONCLUSION: The comparative analysis of its performance with state-of-the-art models and validation of results by radiology experts prove its reliability, robustness, and supremacy. This segmentation phase may prove useful in extracting the features only from the region of interest and improve the accuracy diagnosis.
METHOD: A lot of research works are proposed in the literature for the segmentation of organs using MRI, CT scans, and ultrasound images. But, the segmentation of kidneys, ureters, and bladder from KUB X-ray images is found under explored. Also, there is a lack of validated datasets comprising KUB X-ray images. These challenges motivated the authors to tie up with the team of radiologists and gather the anonymous and validated dataset that can be used to automate the diagnosis of diseases of the urinary system. Further, they proposed a KUB-UNet model for semantic segmentation of the urinary system.
RESULTS: The proposed KUB-UNet model reported the highest accuracy of 99.18% for segmentation of organs of urinary system.
CONCLUSION: The comparative analysis of its performance with state-of-the-art models and validation of results by radiology experts prove its reliability, robustness, and supremacy. This segmentation phase may prove useful in extracting the features only from the region of interest and improve the accuracy diagnosis.