Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning

Looney, Pádraig; Stevenson, Gordon N.; Nicolaides, Kypros H.; Plasencia, W.; Molloholli, M.; Natsis, Stavros; Collins, Sally

doi:10.1172/jci.insight.120178

Cited by 79 publications

(67 citation statements)

References 25 publications

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“…Ultrasound imaging is a standard modality for many diagnostic and monitoring purposes, and there has been significant research into developing automatic methods for segmentation of ultrasound images [5,11]. U-Net [7] for instance has been shown to be a fast and precise solution for medical image segmentation, and has successfully been adapted to segment ultrasound images too [10,1,8,12].…”

Section: Introductionmentioning

confidence: 99%

Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers?

Amiri

Brooks

Rivaz

2019

Lecture Notes in Computer Science

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Fine-tuning a network which has been trained on a large dataset is an alternative to full training in order to overcome the problem of scarce and expensive data in medical applications. While the shallow layers of the network are usually kept unchanged, deeper layers are modified according to the new dataset. This approach may not work for ultrasound images due to their drastically different appearance. In this study, we investigated the effect of fine-tuning different layers of a U-Net which was trained on segmentation of natural images in breast ultrasound image segmentation. Tuning the contracting part and fixing the expanding part resulted in substantially better results compared to fixing the contracting part and tuning the expanding part. Furthermore, we showed that starting to finetune the U-Net from the shallow layers and gradually including more layers will lead to a better performance compared to fine-tuning the network from the deep layers moving back to shallow layers. We did not observe the same results on segmentation of X-ray images, which have different salient features compared to ultrasound, it may therefore be more appropriate to fine-tune the shallow layers rather than deep layers. Shallow layers learn lower level features (including speckle pattern, and probably the noise and artifact properties) which are critical in automatic segmentation in this modality.

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Section: Introductionmentioning

confidence: 99%

Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers?

Amiri

Brooks

Rivaz

2019

Lecture Notes in Computer Science

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“…The system was used to generate first trimester placental volumes, which correlated with infant birth weights at term. While the estimated detection rates of small for gestational age babies (23%) increased over prior nonautomated studies, the system is still not good enough to use as a routine screening tool …”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

“…Deep learning is a subcategory of machine learning; it is based on analyses of actual data and has the capacity to continuously improve its performance with additional data and feedback loops. During 2019, papers began to appear that applied this technology to areas of interest to readers of Prenatal Diagnosis, including improving blastocyst selection for transfer, 20,21 analysis of placental volume, 22 analysis of multi-omics to predict risk of preterm delivery, 23 and reducing the time for analysis and reporting of newborn genome sequencing data. 24 A critical need exists to improve embryo selection in order to increase the chance of a live birth following fertility treatment.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

“…As an organ that is critical for fetal growth and development, it seems that the placenta would be an excellent target for the develop- over prior nonautomated studies, the system is still not good enough to use as a routine screening tool. 22 Prediction of the risk of preterm birth is another major goal of obstetrics. Short cervical length is currently the best available marker, but it has a low positive predictive value.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

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In case you missed it: The Prenatal Diagnosis editors bring you the most significant advances of 2019

et al. 2020

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“…In [9], a semi-automatic method based on the random walker algorithm was proposed to segment the placenta. State-of-the-art segmentation methods using convolutional neural networks (CNNs) have been used in [7,4] and in [13] additionally for the fetus and the gestational sac. These methods focused on early pregnancies between 10-14 weeks of gestational age (GA), when the placenta is small enough to fit in the limited FoV of US.…”

Section: Introductionmentioning

confidence: 99%

Towards Whole Placenta Segmentation at Late Gestation Using Multi-view Ultrasound Images

Zimmer

Gómez

Skelton

et al. 2019

Lecture Notes in Computer Science

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We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.

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Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning

Cited by 79 publications

References 25 publications

Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers?

Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers?

In case you missed it: The Prenatal Diagnosis editors bring you the most significant advances of 2019

Towards Whole Placenta Segmentation at Late Gestation Using Multi-view Ultrasound Images

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