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

Zimmer, Veronika A.; Gómez, Alberto; Skelton, Emily; Toussaint, Nicolas; Zhang, Tong; Khanal, Bishesh; Wright, Robert; Noh, Yohan; Ho, Alison; Matthew, Jacqueline; Hajnal, Joseph V.; Schnabel, Julia A.

doi:10.1007/978-3-030-32254-0_70

Cited by 16 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, classification was applied to differentiate it as normal or placenta previa, which achieved a specificity of 0.82 in 148 cases. Additionally, Zimmer et al [28] proposed a method using 3D US images to segment the placenta at late gestation using 3D CNN. The dataset contained about 127 3D US images, and the model successfully performed multi-view PS, achieving a DSC of 0.8.…”

Section: Placentamentioning

confidence: 99%

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Khan

Aslam

Anis

et al. 2022

Sensors

View full text Add to dashboard Cite

A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.

show abstract

Section: Placentamentioning

confidence: 99%

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Khan

Aslam

Anis

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The proposed method tested on 20 US volumes obtained a DSC and HD of 0.73 and 27 mm, respectively. The work in [95] proposes a three stage pipeline for whole placenta segmentation and volume estimation from multi-view 3D US volumes. The pipeline includes: (i) multi-view acquisition through a multiplexer device that fixes 3 probes in an angle of 30 degree to each other and switches the view from one probe to the other almost in real time; (ii) voxel-wise fusion to combine the multiple views; (iii) segmentation based on a 3D U-Net.…”

Section: Placenta and Amniotic Fluidmentioning

confidence: 99%

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

“…The software has been used for 2D and 3D ultrasound applications. Methods that have used PRETUS in 3D imaging applications include 3D whole-fetus imaging by fast registration of a sequence of 3D ultrasound volumes in real time [15,8], full placenta imaging by fusion and segmentation of the placenta from multiple 3D ultrasound views [9,10], and whole fetal head imaging using atlas-registration and fusion [12,13]. PRETUS has also been used in real time 2D applications, such as standard fetal plane detection [6], automatic biometric measurements in standard fetal planes [16,11], and automatic detection and localization of fetal organs from ultrasound images [14].…”

Section: Motivation and Significancementioning

confidence: 99%

PRETUS: A plug-in based platform for real-time ultrasound imaging research

Gómez¹,

Zimmer²,

Wheeler³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We present PRETUS -a Plugin-based Real Time UltraSound software platform for live ultrasound image analysis and operator support. The software is lightweight; functionality is brought in via independent plug-ins that can be arranged in sequence. The software allows to capture the real-time stream of ultrasound images from virtually any ultrasound machine, applies computational methods and visualises the results on-the-fly.Plug-ins can run concurrently without blocking each other. They can be implemented in C ++ and Python. A graphical user interface can be implemented for each plug-in, and presented to the user in a compact way. The software is free and open source, and allows for rapid prototyping and testing of real-time ultrasound imaging methods in a manufacturer-agnostic fashion. The software is provided with input, output and processing plug-ins, as well as with tutorials to illustrate how to develop new plug-ins for PRETUS.

show abstract

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

Cited by 16 publications

References 14 publications

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

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

PRETUS: A plug-in based platform for real-time ultrasound imaging research

Contact Info

Product

Resources

About