Improving Fetal Head Contour Detection by Object Localisation with Deep Learning

Al-Bander, Baidaa; Alzahrani, Theiab; Alzahrani, Saleh; Williams, Bryan; Zheng, Yalin

doi:10.1007/978-3-030-39343-4_12

Cited by 30 publications

(33 citation statements)

References 20 publications

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“…A possible reason for the lower performance of [28] may be attributed to the challenging task of directly regressing the HC parameters. Our approach also outperformed approaches based on fetal-head segmentation [34], [30]), showing that modeling the HC delineation as a edgedelineation problem, by directly regressing a distance field from the HC, may be a valuable alternative for HC length computation.…”

Section: Discussionmentioning

confidence: 83%

“…These methods could be particularly useful in the clinical practice since multiple plausible semantic segmentation hypotheses are provided to the clinicians, which can choose the best option. In [34], a combined fetal-head localisation and fetal-head segmentation approach based on Mask R-CNN is proposed. In [31], [15] and [34] the HC is then identified by least square ellipse fitting method.…”

Section: Related Workmentioning

confidence: 99%

“…In [34], a combined fetal-head localisation and fetal-head segmentation approach based on Mask R-CNN is proposed. In [31], [15] and [34] the HC is then identified by least square ellipse fitting method.…”

Section: Related Workmentioning

confidence: 99%

“…As a preliminary step, inspired by [27], [31] and [34], we use an object-proposal localization CNN (i.e., the tiny-YOLOv2 [36]) for localizing and centering the fetal head. This way, the regression network is relieved of learning the position of the head.…”

Section: Fetal-head Localization and Centeringmentioning

confidence: 99%

“…For the comparison with the state of the art, we chose the methods proposed in [25], [28], [30] and [34], which follow the deep-learning paradigm and are the most similar with respect to our approach. All these methods were developed and tested using the HC18 Grand Challenge dataset.…”

Section: Ablation Study and Comparison With The State Of The Artmentioning

confidence: 99%

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A regression framework to head-circumference delineation from US fetal images

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

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Background and Objectives Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intraand inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs. Methods The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields. Results The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 (± 1.76) mm and a Dice similarity coefficient of 97.75 (± 1.32) % were achieved, overcoming approaches in the literature. Conclusions The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice.

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Section: Discussionmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Fetal-head Localization and Centeringmentioning

confidence: 99%

Section: Ablation Study and Comparison With The State Of The Artmentioning

confidence: 99%

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A regression framework to head-circumference delineation from US fetal images

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

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Artificial intelligence in obstetric ultrasound: A scoping review

2023

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The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords “neural networks”, OR “artificial intelligence”, OR “machine learning”, OR “deep learning”, AND “obstetrics”, OR “obstetrical”, OR “fetus”, OR “foetus”, OR “fetal”, OR “foetal”, OR “pregnancy”, or “pregnant”, AND “ultrasound” from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors’ search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.

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