Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment

Ryou, Hosuk; Yaqub, Mohammad; Cavallaro, A; Roseman, Fenella; Papageorghiou, Aris T.; Noble, J. Alison

doi:10.1007/978-3-319-47157-0_24

Cited by 32 publications

(34 citation statements)

References 13 publications

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“…And even more so prior work at using CNNs to select or classify US planes. The majority of this work detects US planes of interest from 2D 14,15 or 3D 16,17 video data. For example 14 , was the first to use CNNs for real-time automated detection of 13 fetal standard scan planes, the method uses weak supervision based on image level labels.…”

mentioning

confidence: 99%

“…In 15 , authors used conditional random field models to detect the fetal heart in each frame of the 2D video. Video data information was used to take into account the temporal relationship between the frames 16 . proposed an hybrid method, which uses Random Forests to localize the whole fetus in the sagittal plane and, then CNNs to localize the fetal head, fetal body and non-fetal regions (in axial plane images).…”

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confidence: 99%

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Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

Burgos-Artizzu

Coronado-Gutiérrez

Valenzuela-Alcaráz

et al. 2020

Sci Rep

119

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The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother's cervix (widely used for prematurity screening) and a general category to include any other less common image plane. fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep convolutional neural networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization.

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confidence: 99%

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confidence: 99%

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

Burgos-Artizzu

Coronado-Gutiérrez

Valenzuela-Alcaráz

et al. 2020

Sci Rep

119

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“…The iterative approach regresses transformations that bring the plane closer to the standard plane. This reduces computation cost as ITN selectively samples only a few planes in the 3D volume unlike classification-based methods that require dense sampling [1,3,2,7]. (2) We study the effect on plane detection accuracy using different transformation representations (quaternions, Euler angles, rotation matrix, anchor points) as CNN regression outputs.…”

Section: Contributionsmentioning

confidence: 99%

Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network

Khanal²,

Hou

et al. 2018

Lecture Notes in Computer Science

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Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutional neural network to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the location/orientation of the standard plane in the 3D volume. During inference, the current plane image is passed iteratively to the network until it converges to the standard plane location. We explore the effect of using different transformation representations as regression outputs of ITN. Under a multi-task learning framework, we introduce additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy. When evaluated on 72 US volumes of fetal brain, our method achieves an error of 3.83mm/12.7 • and 3.80mm/12.6 • for the transventricular and transcerebellar planes respectively and takes 0.46s per plane. Source code is publicly available at https://github.com/yuanwei1989/plane-detection.

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“…Recently, several deep-learning-based approaches have been introduced for automatic plane detection. Ryou et al [4] presented the plane detection method for 3D ultrasound axial-images, from the perspective of a classification problem using convolutional neural networks (CNNs). However, this detection method is not suitable to detect a plane that has arbitrary orientation.…”

Section: Introductionmentioning

confidence: 99%

치아 신경관 식별을위한 자동 시상면 검출법

박¹,

신²,

김³

2020

J Korea Comput Graph Soc

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Identification of the mandibular canal path in Computed Tomography (CT) scans is important in dental implantology. Typically, prior to the implant planning, dentists find a sagittal plane where the mandibular canal path is maximally observed, to manually identify the mandibular canal. However, this is time-consuming and requires extensive experience. In this paper, we propose a deep-learning-based framework to detect the desired sagittal plane automatically. This is accomplished by utilizing two main techniques: 1) a modified version of the iterative transformation network (ITN) method for obtaining initial planes, and 2) a fine searching method based on a convolutional neural network (CNN) classifier for detecting the desirable sagittal plane. This combination of techniques facilitates accurate plane detection, which is a limitation of the stand-alone ITN method. We have tested on a number of CT datasets to demonstrate that the proposed method can achieve more satisfactory results compared to the ITN method. This allows dentists to identify the mandibular canal path efficiently, providing a foundation for future research into more efficient, automatic mandibular canal detection methods.

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Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment

Cited by 32 publications

References 13 publications

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network

치아 신경관 식별을위한 자동 시상면 검출법

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