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Journal ArticleDOI

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.

TL;DR: This review covers state‐of‐the‐art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time.
About: This article is published in Medical Image Analysis.The article was published on 2019-01-01. It has received 70 citations till now.
Citations
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Journal ArticleDOI
TL;DR: CA-Net as mentioned in this paper proposes a joint spatial attention module to make the network focus more on the foreground region and a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels.
Abstract: Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at https://github.com/HiLab-git/CA-Net .

205 citations

Journal ArticleDOI
TL;DR: This work makes extensive use of multiple attentions in a CNN architecture and proposes a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time.
Abstract: Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at this https URL

174 citations


Cites background from "Segmentation and classification in ..."

  • ...and the placenta is important for fetal growth assessment and motion correction [41]....

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Journal ArticleDOI
TL;DR: Deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningiomas, no tumor, and pituitary tumor to prove the proposed model's effectiveness.
Abstract: A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.

22 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a simple yet effective residual learning diagnosis system (RLDS) for diagnosing fetal CHD to improve diagnostic accuracy, which adopts convolutional neural networks to extract discriminative features of the fetal cardiac anatomical structures.

20 citations

Journal ArticleDOI
TL;DR: The results suggest that the model used has a high potential to help cardiologists complete the initial screening for fetal congenital heart disease and a strong correlation between the predicted septal defects and ground truth as a mean average precision (mAP).
Abstract: Accurate screening for septal defects is important for supporting radiologists’ interpretative work. Some previous studies have proposed semantic segmentation and object detection approaches to carry out fetal heart detection; unfortunately, the models could not segment different objects of the same class. The semantic segmentation method segregates regions that only contain objects from the same class. In contrast, the fetal heart may contain multiple objects, such as the atria, ventricles, valves, and aorta. Besides, blurry boundaries (shadows) or a lack of consistency in the acquisition ultrasonography can cause wide variations. This study utilizes Mask-RCNN (MRCNN) to handle fetal ultrasonography images and employ it to detect and segment defects in heart walls with multiple objects. To our knowledge, this is the first study involving a medical application for septal defect detection using instance segmentation. The use of MRCNN architecture with ResNet50 as a backbone and a 0.0001 learning rate allows for two times faster training of the model on fetal heart images compared to other object detection methods, such as Faster-RCNN (FRCNN). We demonstrate a strong correlation between the predicted septal defects and ground truth as a mean average precision (mAP). As shown in the results, the proposed MRCNN model achieves good performance in multiclass detection of the heart chamber, with 97.59% for the right atrium, 99.67% for the left atrium, 86.17% for the left ventricle, 98.83% for the right ventricle, and 99.97% for the aorta. We also report competitive results for the defect detection of holes in the atria and ventricles via semantic and instance segmentation. The results show that the mAP for MRCNN is about 99.48% and 82% for FRCNN. We suggest that evaluation and prediction with our proposed model provide reliable detection of septal defects, including defects in the atria, ventricles, or both. These results suggest that the model used has a high potential to help cardiologists complete the initial screening for fetal congenital heart disease.

19 citations


Cites background or methods from "Segmentation and classification in ..."

  • ...Unfortunately, such methods (with threshold-based techniques, for example) yield the best results when the regions of interest in an image exhibit a massive difference in strength from the background of the image, but this results in more similar images with problems, dramatically reducing the efficiency and decreasing the applicability of these methods [6], [27]....

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  • ...It can aid doctors in making more accurate treatment plans [27]....

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  • ...The segmentation process is the key to exploring fetal heart abnormalities, especially defect conditions [27]....

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References
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Journal ArticleDOI
TL;DR: Fetoscopic laser coagulation for TTTS performed between 26 + 0 and 28 + 6 weeks of gestation was associated with similar outcomes as those observed in cases treated before 26 weeks.
Abstract: Objective: To compare the outcomes of twin-to-twin transfusion syndrome (TTTS) cases treated with fetoscopic laser coagulation of vascular anastomoses before 25 + 6 weeks of gestati

35 citations

Proceedings ArticleDOI
29 Apr 2014
TL;DR: This paper proposes a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors and shows how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data.
Abstract: Automatic detection of the fetal brain in Magnetic Resonance (MR) Images is especially difficult due to arbitrary orientation of the fetus and possible movements during the scan. In this paper, we propose a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors. We calculate features for a set of 50 prenatal fast spin echo T2 volumes of the uterus and learn the appearance of the fetal brain in the feature space. We evaluate our novel classification method and show that we can localize the fetal brain with an accuracy of 100% and classify fetal brain voxels with an accuracy above 97%. Furthermore, we show how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data leading to a final segmentation with a Dice score above 0.90.

34 citations

Journal ArticleDOI
TL;DR: An extended visualization system with the fusion of endoscopic image mosaics with a 3-D ultrasound-image model that may provide an improved and efficient way of planning and guidance in laser photocoagulation treatment is developed.
Abstract: This paper describes a fast image mapping system that integrates endoscopic image mosaics with three-dimensional (3-D) ultrasound images for assisting intrauterine laser photocoagulation treatment....

34 citations

Journal ArticleDOI
TL;DR: The effects of olighydramnios on lung development, the mechanisms of oligohydramnio-induced pulmonary hypoplasia identified in various animal studies, and the long-term respiratory outcomes in childhood of olig carbohydrates-exposed fetuses reported by a population-based study are described.
Abstract: Pulmonary hypoplasia is a substantial cause of death in newborn infants, and oligohydramnios is one of the most commonly associated abnormalities. Lung growth is influenced by physical factors such as the intrauterine space, lung liquid volume and pressure, and fetal breathing movements. During lung development, the main physical force experienced by the lungs is stretching induced by breathing movements and the lung fluid in the airspaces. Oligohydramnios reduces the intrathoracic cavity size, thus disrupting fetal lung growth and leading to pulmonary hypoplasia. The exact mechanism by which oligohydramnios alters the respiratory system structure and the effect of oligohydramnios on long-term respiratory outcomes remain unknown. In this review, we summarize the effects of oligohydramnios on lung development, discuss the mechanisms of oligohydramnios-induced pulmonary hypoplasia identified in various animal studies, and describe the long-term respiratory outcomes in childhood of oligohydramnios-exposed fetuses reported by a population-based study.

33 citations

Journal ArticleDOI
TL;DR: A novel variational segmentation framework combining shape priors and parametric intensity distribution modeling for extracting the fetal envelope on 3D obstetric ultrasound images is presented.

32 citations