Automated segmentation of the gastrocnemius and soleus in shank ultrasound images through deep residual neural network

Du, Getao; Zhan, Yonghua; Zhang, Yue; Guo, Jianzhong; Chen, Xueli; Liang, Jimin; Zhao, Heng

doi:10.1016/j.bspc.2021.103447

Cited by 1 publication

(6 citation statements)

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“…Following the review of the title and abstract and, upon necessity, the full text further 187 records were rejected. After the reviewing process, a total of 36 original papers [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] and 20 conference proceedings [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ,…”

Section: Resultsmentioning

confidence: 99%

“…Despite US imaging being widely used to study the musculoskeletal system in clinical studies, there is still only a partial exploration of this imaging modality in preclinical investigations. Indeed, only five [ 35 , 38 , 42 , 55 , 61 ] papers (8.9%) proposed deep learning architectures to work with US images of bones [ 38 , 61 ], muscles [ 35 , 55 ] and teeth [ 38 , 55 ].…”

Section: Resultsmentioning

confidence: 99%

“…Pig is the preclinical model chosen in the majority of the papers in this section [ 35 , 38 , 55 ], and only two papers proposed studies on rats [ 42 ] and rabbits [ 61 ].…”

Section: Resultsmentioning

confidence: 99%

“…All these papers [ 35 , 38 , 42 , 55 , 61 ] trained DL systems for segmentation tasks and all of them proposed U-Net-based models properly adapted for the specific training tasks. In [ 35 ], Carson et al proposed a system able to detect, segment, classify, and display neural structures during trans-psoas spine surgery by processing B-mode images.…”

Section: Resultsmentioning

confidence: 99%

“…The authors integrated a U-Net in their AI system to classify bone and muscle regions in B-mode images surrounding the muscle psoas. A U-Net modified (called ResTU-net) has been proposed in [ 42 ] for the segmentation of muscles (gastrocnemius and soleus) in the rat hindlimb. The performances DSC of 94.82% and 90.72% achieved for gastrocnemius and soleus, respectively, outperformed the state-of-the-art methods.…”

Section: Resultsmentioning

confidence: 99%

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Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

De Rosa,

L’Abbate,

Kusmic

et al. 2023

Life

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Background and Aim: Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models. Methods: A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines. Results: Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging. Conclusion: DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Pig is the preclinical model chosen in the majority of the papers in this section [ 35 , 38 , 55 ], and only two papers proposed studies on rats [ 42 ] and rabbits [ 61 ].…”

Section: Resultsmentioning

confidence: 99%