A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos

Qiu, Ziming; Xu, Tongda; Langerman, Jack; Das, William; Wang, Chuiyu; Nair, Nitin; Aristizábal, Orlando; Mamou, Jonathan; Turnbull, Daniel H.; Ketterling, Jeffrey A.; Wang, Yao

doi:10.1109/tuffc.2021.3068156

Cited by 9 publications

(11 citation statements)

References 41 publications

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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%

“…This last paragraph collects 13 papers [ 28 , 32 , 33 , 36 , 46 , 57 , 58 , 71 , 74 , 78 , 79 , 80 , 82 ] that proposed heterogeneous US-based applications adopting DL systems. In particular, papers focused on embryo segmentation [ 28 , 57 , 79 , 80 , 82 ] and embryo reconstruction [ 78 ] or on the vascularisation of breast cancer tissue [ 32 , 46 ], lymph node [ 58 ], hind limb [ 33 ], chorioallantoic membrane [ 36 ] and lung [ 71 , 74 ].…”

Section: Resultsmentioning

confidence: 99%

“…Nine papers [ 28 , 32 , 33 , 46 , 57 , 58 , 79 , 80 , 82 ] adopted murine models, two [ 36 , 78 ] were working with chicken US images and two [ 71 , 74 ] with pig models.…”

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%

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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

View full text Add to dashboard Cite

show abstract

“…Shen et al ( 2022a ) developed an automated stain-free detection method for detecting embryo polarization using a deep CNN binary classification model, achieving an accuracy of 85% and significantly outperforming human volunteers trained on the same data (61% accuracy). In addition, Qiu et al ( 2021 ) introduced a deep learning pipeline for automating the segmentation and classification of high-frequency ultrasound images of mouse embryo brain ventricles and bodies based on a volumetric CNN classifier, thus providing a powerful tool for developmental biologists.…”

Section: Ai In Animal Developmentmentioning

confidence: 99%

From beasts to bytes: Revolutionizing zoological research with artificial intelligence

Zhang,

Luo,

Sun

et al. 2023

Zoological Research

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Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.

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“…They compared their model with 3D U-Net 16 and demonstrated Dice score improvements of at least 7% across all abdominal organs of interest. Qiu et al 17 , 18 utilized the first stage VNet to generate a low resolution segmentation of mouse embryo brain ventricle 19 – 21 and body in 3D high frequency ultrasound images, followed by refinement using a second VNet in high resolution at the second stage. Isensee et al 22 hypothesized that a well-configured U-Net is still difficult to surpass.…”

Section: Introductionmentioning

confidence: 99%

Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks

Liu

Gargesha

Scott

et al. 2022

Sci Rep

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Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D-slices and 3D U-Net with either 3D-whole-mouse or 3D-patches. We evaluated the brain, thymus, lung, heart, liver, stomach, spleen, left and right kidney, and bladder. Training with 63 mice, 2D-slices had the best performance, with median Dice scores of > 0.9 and median Hausdorff distances of < 1.2 mm in eightfold cross validation for all organs, except bladder, which is a problem organ due to variable filling and poor contrast. Results were comparable to those for a second analyst on the same data. Regression analyses were performed to fit learning curves, which showed that 2D-slices can succeed with fewer samples. Review and editing of 2D-slices segmentation results reduced human operator time from ~ 2-h to ~ 25-min, with reduced inter-observer variability. As demonstrations, we used organ segmentation to evaluate size changes in liver disease and to quantify the distribution of therapeutic mesenchymal stem cells in organs. With a 48-GB GPU, we determined that extra GPU RAM improved the performance of 3D deep learning because we could train at a higher resolution.

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A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos

Cited by 9 publications

References 41 publications

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

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

From beasts to bytes: Revolutionizing zoological research with artificial intelligence

Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks

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