Enhancing Kidney Outlines in ۲D Ultrasound Images Using Deep Learning

Renal ultrasound (US) imaging, also known as a kidney ultrasound, serves as a pivotal diagnostic tool for evaluating renal health. It plays a critical role in various aspects of patient care, including diagnosis, treatment planning, surgical intervention, and post-treatment monitoring, allowing healthcare providers to assess both the kidneys and bladder. Furthermore, segmentation of kidney ultrasound (US) images is essential in extracting relevant objects or areas from the complete image, aiding in the evaluation of tissue organization and improving diagnostic accuracy. While manual delineation of kidneys in US images presents challenges in clinical settings due to complexity and tedium, this study introduces a novel approach to enhance the delineation of kidney outlines in ۲D ultrasound images using deep learning techniques. The method involves concatenating two DeeplabV۳ models, leveraging ResNet-۱۸ and ResNet-۱۵۲ backbones. Evaluation of the method yielded promising results, with precision, recall, F۱ score, accuracy, and Jaccard index reported at ۰.۹۷۸, ۰.۹۹۳, ۰.۹۸۵, ۰.۹۹۷, and ۰.۹۷۱ respectively, accompanied by standard deviations of ±۰.۰۰۹, ±۰.۰۰۵, ±۰.۰۰۵, ±۰.۰۰۱, and ±۰.۰۰۹, our findings underscore the efficacy of the proposed method in augmenting kidney outline detection in ۲D ultrasound images. This advancement holds significant promise for enhancing clinical diagnosis and treatment planning in renal health management.

کلیدواژه ها:

Renal ultrasound ، kidney ultrasound ، Segmentation ، Deep Learning ، DeeplabV۳Plus ، medical image processing