Variance‐aware attention U‐Net for multi‐organ segmentation

Lin, Haoneng; Li, Zongshang; Yang, Zefan; Wang, Yi

doi:10.1002/mp.15322

Cited by 25 publications

(14 citation statements)

References 45 publications

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“…The experimental results demonstrate that our network can effectively improve the segmentation when the labeled data are limited. In our future work, we still have to optimize our backbone network by comprehensively analyzing the local and global contextual information 42 to improve the segmentation accuracy.…”

Section: Discussionmentioning

confidence: 99%

A reciprocal learning strategy for semisupervised medical image segmentation

et al. 2022

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Background: Semisupervised strategy has been utilized to alleviate issues from segmentation applications due to challenges in collecting abundant annotated segmentation masks, which is an essential prerequisite for training high-performance 3D convolutional neural networks (CNNs) . Purpose: Existing semisupervised segmentation methods are mainly concerned with how to generate the pseudo labels with regularization but not evaluate the quality of the pseudo labels explicitly. To alleviate this problem, we offer a simple yet effective reciprocal learning strategy for semisupervised volumetric medical image segmentation, which generates more reliable pseudo labels for the unannotated data. Methods: Our proposed reciprocal learning is achieved through a pair of networks,one as a teacher network and the other as a student network.The student network learns from pseudo labels generated by the teacher network. In addition, the teacher network autonomously optimizes its parameters based on the reciprocal feedback signals from the student's performance on the annotated images. The efficacy of the proposed method is evaluated on three medical image data sets, including 82 pancreas computed tomography (CT) scans (training/testing: 62/20), 100 left atrium gadolinium-enhanced magnetic resonance (MR) scans (training/testing: 80/20), and 200 breast cancer MR scans (training/testing: 68/132). The comparison methods include mean teacher (MT) model, uncertainty-aware MT (UA-MT) model, shape-aware adversarial network (SASSNet), and transformation-consistent self -ensembling model (TCSM). The evaluation metrics are Dice similarity coefficient (Dice), Jaccard index (Jaccard), 95% Hausdorff distance (95HD), and average surface distance (ASD). The Wilcoxon signed-rank test is used to conduct the statistical analyses. Results: By utilizing 20% labeled data and 80% unlabeled data for training, our proposed method achieves an average Dice of 84.77%/90.46%/78.53%, Jaccard of 73.71%/82.67%/69.00%, ASD of 1.58/1.90/0.57, and 95HD of 6.24/5.97/4.34 on pancreas/left atrium/breast data sets, respectively. These results outperform several cutting-edge semisupervised approaches, showing

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Section: Discussionmentioning

confidence: 99%

A reciprocal learning strategy for semisupervised medical image segmentation

et al. 2022

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“…Attention U-Net was published in 2018 [ 20 ]; it was verified to perform well in several medical image segmentation tasks [ 21 , 22 ], but it has not yet been employed in brain hematoma segmentation.…”

Section: Methodsmentioning

confidence: 99%

Automatic and Efficient Prediction of Hematoma Expansion in Patients with Hypertensive Intracerebral Hemorrhage Using Deep Learning Based on CT Images

Wang

Gao

et al. 2022

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Patients with hypertensive intracerebral hemorrhage (ICH) have a high hematoma expansion (HE) incidence. Noninvasive prediction HE helps doctors take effective measures to prevent accidents. This study retrospectively analyzed 253 cases of hypertensive intraparenchymal hematoma. Baseline non-contrast-enhanced CT scans (NECTs) were collected at admission and compared with subsequent CTs to determine the presence of HE. An end-to-end deep learning method based on CT was proposed to automatically segment the hematoma region, region of interest (ROI) feature extraction, and HE prediction. A variety of algorithms were employed for comparison. U-Net with attention performs best in the task of segmenting hematomas, with the mean Intersection overUnion (mIoU) of 0.9025. ResNet-34 achieves the most robust generalization capability in HE prediction, with an area under the receiver operating characteristic curve (AUC) of 0.9267, an accuracy of 0.8827, and an F1 score of 0.8644. The proposed method is superior to other mainstream models, which will facilitate accurate, efficient, and automated HE prediction.

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“…To demonstrate the efficacy of our method leveraging few labeled data, we conducted training using 10 labeled data (denoted as RoR 10 ) and only 1 labeled data (denoted as RoR 1 ), respectively. 2 In addition, considering the idea of the saliency map is similar to the attention map used in the segmentation task, [33][34][35] we further included attention U-net 33 in the comparison. Specifically, we trained the attention U-net and used its generated attention map as saliency map in our method.…”

Section: Experimental Methodsmentioning

confidence: 99%

Salient deformable network for abdominal multiorgan registration

2022

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Background: Image registration has long been an active research area in the society of medical image computing, which is to perform spatial transformation between a pair of images and establish a point-wise correspondence to achieve spatial consistency. Purpose: Previous work mainly focused on learning complicated deformation fields by maximizing the global-level (i.e., foreground plus background) image similarity.We argue that taking the background similarity into account may not be a good solution, if we only seek the accurate alignment of target organs/regions in real clinical practice. Methods: We, therefore, propose a novel concept of Salient Registration and introduce a novel deformable network equipped with a saliency module. Specifically, a multitask learning-based saliency module is proposed to discriminate the salient regions-of -registration in a semisupervised manner. Then, our deformable network analyzes the intensity and anatomical similarity of salient regions, and finally conducts the salient deformable registration. Results: We evaluate the efficacy of the proposed network on challenging abdominal multiorgan CT scans. The experimental results demonstrate that the proposed registration network outperforms other state-of -the-art methods, achieving a mean Dice similarity coefficient (DSC) of 40.2%,Hausdorff distance (95 HD) of 20.8 mm, and average symmetric surface distance (ASSD) of 4.58 mm. Moreover, even by training using one labeled data, our network can still attain satisfactory registration performance, with a mean DSC of 39.2%, 95 HD of 21.2 mm, and ASSD of 4.78 mm. Conclusions:The proposed network provides an accurate solution for multiorgan registration and has the potential to be used for improving other registration applications. The code is publicly available at https://github.com/Rrrfrr/Salient-Deformable-Network.

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Variance‐aware attention U‐Net for multi‐organ segmentation

Cited by 25 publications

References 45 publications

A reciprocal learning strategy for semisupervised medical image segmentation

A reciprocal learning strategy for semisupervised medical image segmentation

Automatic and Efficient Prediction of Hematoma Expansion in Patients with Hypertensive Intracerebral Hemorrhage Using Deep Learning Based on CT Images

Salient deformable network for abdominal multiorgan registration

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