Automatic segmentation in fetal ultrasound images based on improved U-net

Yang, Yujie; Yang, Pinli; Zhang, Bo

doi:10.1088/1742-6596/1693/1/012183

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Due to the large differences in tumor location and size in different images, the network needs to have sufficient receptive field and powerful spatial multi-scale processing capability. However, unlike Atrous Spatial Pyramid Pooling (ASPP), U-Net cannot master as much semantic information of different scales as possible, nor can it satisfy the accurate segmentation of the region near the target edge in the image [ 17 ]. Therefore, the FPN structure is mainly added to U-Net in this paper to improve U-Net's ability to integrate multi-scale semantic information and enrich the information contained in the features used in pixel tag classification.…”

Section: Methodsmentioning

confidence: 99%

Brain tumor image segmentation based on improved FPN

Sun,

Yang,

Chen

et al. 2023

BMC Med Imaging

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Purpose Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. Materials and methods Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. Results Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. Conclusions The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors.

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Section: Methodsmentioning

confidence: 99%

Brain tumor image segmentation based on improved FPN

Sun,

Yang,

Chen

et al. 2023

BMC Med Imaging

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“…118,119 Two studies focused on amniotic fluid with one evaluating automatic measurement of amniotic fluid index 120 and the other assessing segmentation of amniotic fluid and fetal tissue. 121 The remaining studies included a study to assess machine learning to determine occiput anterior versus occipitoposterior position in the second stage of labor, 122 machine learning assessment of fetal-lung texture in pregnancies affected by gestational diabetes or preeclampsia compared to normal pregnancies, 123 use of an AI classifier to recognize fetal facial expressions on 4D ultrasound, 124 detection of the FASP, 125 automatic detection of the fetal face on 3D ultrasound, 126 assessment of fetal presentation and confirmation of fetal cardiac activity, 127 segmentation of the fetal thoracic wall, 128 classification of the umbilical cord into normocoiling, hypocoiling and hypercoiling, 129 automated grading of hydronephrosis on ultrasound, 130 segmentation of the fetal kidneys, 131 segmentation of the AC and FL, 132 assessment of an image reconstruction framework applied to the whole fetus 133 and automated detection of fetal standard planes. 134,135 A summary of results is reported in Table 8.…”

Section: Number Of Patients Inclusion Criteria Description Of Artific...mentioning

confidence: 99%

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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“…It has been stated that the proposed method has a great potential to support physicians. Yang et al (2020) used Residual U-Net and ASPP U-net models for automatic segmentation of biometric parameters AC, FL and CRL. Residual U-Net was used for the gradient problem and ASPP U-Net was used to increase the accuracy of the segmentation without increasing the depth of the model.…”

Section: Deep Learningmentioning

confidence: 99%

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

Ataş¹,

Yılmaz²

2022

acin

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Computer aided diagnostic methods have been helping medical experts for monitoring fetus health for many years. The use of new methods has made a positive effect in monitoring the health of fetus as well as the diagnosis of anomalies. This study first introduces the indicators for identifying anomalies in fetus and gives basic information about computer-based methods such as traditional image processing, machine learning and deep learning. Then an overview of existing studies which use novel techniques on monitoring fetus health and anomaly detection from ultrasound images is given. Finally, the main challenges of novel techniques and future directions of research on computer-aided monitoring of fetus health are summarized.

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Automatic segmentation in fetal ultrasound images based on improved U-net

Cited by 6 publications

References 10 publications

Brain tumor image segmentation based on improved FPN

Brain tumor image segmentation based on improved FPN

Artificial intelligence in obstetric ultrasound: A scoping review

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

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