Automated Placenta Segmentation with a Convolutional Neural Network Weighted by Acoustic Shadow Detection

Hu, Ricky; Singla, Rohit; Yan, Ryan; Mayer, Chantal; Rohling, Robert

doi:10.1109/embc.2019.8857448

Cited by 19 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, the segmentation model in this work was largely optimized for efficiency over performance, so that it could be deployed on a smartphone in a clinical setting. In contrast to Hu et al (2019), our aim was not to achieve the best segmentation Dice score but to create an efficient method for automatic placenta localization.…”

Section: Discussionmentioning

confidence: 99%

“…Several other studies have been conducted on automatic placental segmentation from ultrasound imaging. Looney et al (2018) and Yang et al (2019) attempted volumetric (3-D) placental segmentation, whereas Hu et al (2019) considered 2-D placental segmentation. Hu et al (2019) achieved a mean Dice score of 0.92 with a U-Net trained on 1364 2-D images acquired from 247 cases.…”

Section: Introductionmentioning

confidence: 99%

“…Looney et al (2018) and Yang et al (2019) attempted volumetric (3-D) placental segmentation, whereas Hu et al (2019) considered 2-D placental segmentation. Hu et al (2019) achieved a mean Dice score of 0.92 with a U-Net trained on 1364 2-D images acquired from 247 cases. The gestational age of these cases ranged from 8 to 34 wk.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning

Schilpzand

Neff

Dillen

et al. 2022

Ultrasound in Medicine & Biology

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning

Schilpzand

Neff

Dillen

et al. 2022

Ultrasound in Medicine & Biology

View full text Add to dashboard Cite

“…A number of approaches performs placenta segmentation from 2D US images. The approach proposed in [89] uses a U-Net inspired CNN. The CNN is modified adding a layer to detect acoustic shadow and improve the segmentation accuracy.…”

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

“…Since the image-level labels are actually also expensive and difficult to collect due to ambiguity and semi-transparency of shadows, in this study, we focus on utilizing unlabeled data supported by coarse domain-specific knowledge. A combination of the traditional shadow detecting method [19] and DNN based segmentation for US images is also proposed [24]. It shows that the segmentation results can be improved by knowing the presence of shadows and it is important to detect shadow precisely.…”

Section: Related Workmentioning

confidence: 99%

Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows

et al. 2021

View full text Add to dashboard Cite

Acoustic shadows are common artifacts in medical ultrasound imaging. The shadows are caused by objects that reflect ultrasound such as bones, and they are shown as dark areas in ultrasound images. Detecting such shadows is crucial for assessing the quality of images. This will be a pre-processing for further image processing or recognition aiming computer-aided diagnosis. In this paper, we propose an auto-encoding structure that estimates the shadowed areas and their intensities. The model once splits an input image into an estimated shadow image and an estimated shadow-free image through its encoder and decoder. Then, it combines them to reconstruct the input. By generating plausible synthetic shadows based on relatively coarse domain-specific knowledge on ultrasound images, we can train the model using unlabeled data. If pixel-level labels of the shadows are available, we also utilize them in a semi-supervised fashion. By experiments on ultrasound images for fetal heart diagnosis, we show that our method achieved 0.720 in the DICE score and outperformed conventional image processing methods and a segmentation method based on deep neural networks. The capability of the proposed method on estimating the intensities of shadows and the shadow-free images is also indicated through the experiments.

show abstract

Automated Placenta Segmentation with a Convolutional Neural Network Weighted by Acoustic Shadow Detection

Cited by 19 publications

References 16 publications

Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning

Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning

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

Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows

Contact Info

Product

Resources

About