Multi-task deep learning for segmentation and landmark detection in obstetric sonography

Allan, Michael B.; Jafari, Mohammad H.; Woudenberg, Nathan Van; Frenkel, Oron; Murphy, Darra; Wee, Tracee; D'Ortenzio, Rob; Wu, Yue; Roberts, James M.; Shatani, Naoya; Gu, Ang Nan; Sojoudi, Samira; Abolmaesumi, Purang

doi:10.1117/12.2611163

Cited by 2 publications

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“…A portable ultrasound system is also used in healthcare facilities to assist in intravascular procedures [ 81 , 82 ]. Obstetrics and gynecology use ultrasound systems on a daily basis to examine and mentor pregnant women’s health and fetus growth [ 83 , 84 ]. Furthermore, ultrasound is also used to detect ovarian tumours, which is one of the main diseases that affect women’s health [ 85 ].…”

Section: Overview Of Ultrasound Imaging In Speech Recognitionmentioning

confidence: 99%

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

et al. 2022

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Lingual ultrasound imaging is essential in linguistic research and speech recognition. It has been used widely in different applications as visual feedback to enhance language learning for non-native speakers, study speech-related disorders and remediation, articulation research and analysis, swallowing study, tongue 3D modelling, and silent speech interface. This article provides a comparative analysis and review based on quantitative and qualitative criteria of the two main streams of tongue contour segmentation from ultrasound images. The first stream utilizes traditional computer vision and image processing algorithms for tongue segmentation. The second stream uses machine and deep learning algorithms for tongue segmentation. The results show that tongue tracking using machine learning-based techniques is superior to traditional techniques, considering the performance and algorithm generalization ability. Meanwhile, traditional techniques are helpful for implementing interactive image segmentation to extract valuable features during training and postprocessing. We recommend using a hybrid approach to combine machine learning and traditional techniques to implement a real-time tongue segmentation tool.

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Section: Overview Of Ultrasound Imaging In Speech Recognitionmentioning

confidence: 99%

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

et al. 2022

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“…18 nnU-net in obtained state-of-the-art results on 23 different datasets and tasks spanning many organs and modalities; however, ultrasound was not investigated. There is a substantial amount of literature on segmenting anatomy from ultrasound images, ranging from obstetrics and the pelvic floor, 19 fetal anatomy, 20,21 cardiac anatomy and landmarks, 22 and organs such as prostate 23 and liver. 24 Specific to kidney ultrasound, Yin et al 25 recently created a fully automatic segmentation neural network for detecting the kidney's boundary that incorporated learned boundary distance maps to then optimize pixelwise labels.…”

Section: Introductionmentioning

confidence: 99%

Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers

Singla

Ringstrom

et al. 2023

J. Med. Imag.

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.PurposeLength and width measurements of the kidneys aid in the detection and monitoring of structural abnormalities and organ disease. Manual measurement results in intra- and inter-rater variability, is complex and time-consuming, and is fraught with error. We propose an automated approach based on machine learning for quantifying kidney dimensions from two-dimensional (2D) ultrasound images in both native and transplanted kidneys.ApproachAn nnU-net machine learning model was trained on 514 images to segment the kidney capsule in standard longitudinal and transverse views. Two expert sonographers and three medical students manually measured the maximal kidney length and width in 132 ultrasound cines. The segmentation algorithm was then applied to the same cines, region fitting was performed, and the maximum kidney length and width were measured. Additionally, single kidney volume for 16 patients was estimated using either manual or automatic measurements.ResultsThe experts resulted in length of 84.8 ± 26.4 mm [95% CI: 80.0, 89.6] and a width of 51.8 ± 10.5 mm [49.9, 53.7]. The algorithm resulted a length of 86.3 ± 24.4 [81.5, 91.1] and a width of 47.1 ± 12.8 [43.6, 50.6]. Experts, novices, and the algorithm did not statistically significant differ from one another (p > 0.05). Bland–Altman analysis showed the algorithm produced a mean difference of 2.6 mm (SD = 1.2) from experts, compared to novices who had a mean difference of 3.7 mm (SD = 2.9 mm). For volumes, mean absolute difference was 47 mL (31%) consistent with ∼1 mm error in all three dimensions.ConclusionsThis pilot study demonstrates the feasibility of an automatic tool to measure in vivo kidney biometrics of length, width, and volume from standard 2D ultrasound views with comparable accuracy and reproducibility to expert sonographers. Such a tool may enhance workplace efficiency, assist novices, and aid in tracking disease progression.

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Multi-task deep learning for segmentation and landmark detection in obstetric sonography

Cited by 2 publications

References 5 publications

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers

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