Confident Head Circumference Measurement from Ultrasound with Real-Time Feedback for Sonographers

Budd, Samuel; Sinclair, Matthew; Khanal, Bishesh; Matthew, Jacqueline; Lloyd, David; Gómez, Alberto; Toussaint, Nicolas; Kainz, Bernhard

doi:10.1007/978-3-030-32251-9_75

Cited by 27 publications

(28 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future directions of this work include exploring feedback from users, as recently proposed in [33]. Moreover, visual attention mechanism could be encoded in the regression network for further boosting the results [41].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The work in [33] develops two probabilistic CNN methods: Monte Carlo Dropout during inference and a Probabilistic UNet. An ensemble of the generated segmentation masks is used to reject acquired images that produce sub-optimal HC measurements.…”

Section: Related Workmentioning

confidence: 99%

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

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

show abstract

“…A popular approach to account for the uncertainty in the learned model parameters is to use variational Bayesian methods, which are a family of techniques for approximating Bayesian inference over the network weights. Budd et al [19] proposed to use this approach to automatically estimate fetal Head Circumference from Ultrasound imaging and provide real-time feedback on measurement robustness. Another approach is to probabilistic graphical models to model the conditional segmentation masks given an input image using a conditional variational autoencoder (cVAE) approach.…”

Section: Background/introductionmentioning

confidence: 99%

Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control

Puyol‐Antón

Ruijsink

Baumgartner

et al. 2020

Journal of Cardiovascular Magnetic Resonance

Self Cite

View full text Add to dashboard Cite

Background: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T 1 mapping in particular has shown promise as a useful biomarker to support diagnostic, therapeutic and prognostic decision-making in ischaemic and non-ischaemic cardiomyopathies. Methods: Convolutional neural networks (CNNs) with Bayesian inference are a category of artificial neural networks which model the uncertainty of the network output. This study presents an automated framework for tissue characterisation from native shortened modified Look-Locker inversion recovery ShMOLLI T 1 mapping at 1.5 T using a Probabilistic Hierarchical Segmentation (PHiSeg) network (PHCUMIS 119-127, 2019). In addition, we use the uncertainty information provided by the PHiSeg network in a novel automated quality control (QC) step to identify uncertain T 1 values. The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients (N=100 for the PHiSeg network and N=700 for the QC). We used the proposed method to obtain reference T 1 ranges for the left ventricular (LV) myocardium in healthy subjects as well as common clinical cardiac conditions. Results: T 1 values computed from automatic and manual segmentations were highly correlated (r=0.97). Bland-Altman analysis showed good agreement between the automated and manual measurements. The average Dice metric was 0.84 for the LV myocardium. The sensitivity of detection of erroneous outputs was 91%. Finally, T 1 values were automatically derived from 11,882 CMR exams from the UK Biobank. For the healthy cohort, the mean (SD) corrected T 1 values were 926.61 (45.26), 934.39 (43.25) and 927.56 (50.36) for global, interventricular septum and free-wall respectively.

show abstract

“…The work in [139] further extends the research in the field of HC estimation by developing two probabilistic CNN methods: Monte Carlo Dropout during inference and a probabilistic U-Net. These methods are particularly useful in the clinical practice since multiple plausible semantic segmentations of fetal heads along with HC measurements are provided to the clinicians, which can choose the best option.…”

Section: Biometry Parameter Estimationmentioning

confidence: 84%

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Fiorentino¹,

Villani²,

Cosmo³

et al. 2022

Preprint

View full text Add to dashboard Cite

Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite the large number of survey papers already present in this field, most of them are focusing on a broader area of medicalimage analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 145 research papers published after 2017. Each paper is analyzed and commented from both the methodology and application perspective. We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice.

show abstract

Confident Head Circumference Measurement from Ultrasound with Real-Time Feedback for Sonographers

Cited by 27 publications

References 13 publications

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

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

Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Contact Info

Product

Resources

About