4D ultrasound adaptive image pre-processing

Elnokrashy, Ahmed F.; Kadah, YasserM.

doi:10.1109/nrsc.2018.8354366

Cited by 2 publications

(1 citation statement)

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“…Ultrasonography has transformative potential for measuring muscle health with the emerging interest in documenting and understanding muscle atrophy and function in every patient condition [5]. Additionally, ultrasound characteristics pose specific challenges in 1D [6], 2D [7], 3D [8], and 4D [9] images, such as image quality due to noise, scarce data, low image quality, and hand-held or operator skill, which may have a substantial influence on the efficacy of deep learning performance when compared with X-ray, computed tomography (CT), or magnetic resonance imaging (MRI) images [10] due to the special attention and resources required in the deep learning process. Furthermore, the challenges in the muscle ultrasound imaging process include the identification of landmarks, reliability testing, muscle site tracking, image acquisition and analysis, equipment use, normative data, and the interpretation of results [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images

et al. 2021

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Deep learning has aided in the improvement of diagnosis identification, evaluation, and the interpretation of muscle ultrasound images, which may benefit clinical personnel. Muscle ultrasound images presents challenges such as low image quality due to noise, insufficient data, and different characteristics between skeletal and smooth muscles that can affect the effectiveness of deep learning results. From 2018 to 2020, deep learning has the improved solutions used to overcome these challenges; however, deep learning solutions for ultrasound images have not been compared to the conditions and strategies used to comprehend the current state of knowledge for handling skeletal and smooth muscle ultrasound images. This study aims to look at the challenges and trends of deep learning performance, especially in regard to overcoming muscle ultrasound image problems such as low image quality, muscle movement in skeletal muscles, and muscle thickness in smooth muscles. Skeletal muscle segmentation presents difficulties due to the regular movement of muscles and resulting noise, recording data through skipped connections, and modified layers required for upsampling. In skeletal muscle classification, the problems faced are area-specific, thus making a cropping strategy useful. Furthermore, there is no need to add additional layer modifications for smooth muscle segmentation as muscle thickness is the main problem in such cases.

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