Contour-Aware Loss: Boundary-Aware Learning for Salient Object Segmentation

Chen, Zixuan; Zhou, Hongmin; Lai, Jianhuang; Yang, Lei; Xie, Xiaohua

doi:10.1109/tip.2020.3037536

Cited by 84 publications

(28 citation statements)

References 47 publications

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“…Our multitask edge‐aware learning can be thought of as a boundary‐enhanced technique. Although other techniques such as simply assigning more weights on boundary in loss calculation 43 or using focal loss 44 may have similar effects, the advantage of our approach is that it can add auxiliary tasks such as classification task 41 and distance‐prediction task 45 to further boost model performance.…”

Section: Discussionmentioning

confidence: 99%

Automatic segmentation of hippocampus in hippocampal sparing whole brain radiotherapy: A multitask edge‐aware learning

Qiu

Yang

et al. 2021

Medical Physics

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This study aimed to improve the accuracy of the hippocampus segmentation through multitask edge-aware learning. Method: We developed a multitask framework for computerized hippocampus segmentation. We used three-dimensional (3D) U-net as our backbone model with two training objectives: (a) to minimize the difference between the targeted binary mask and the model prediction; and (b) to optimize an auxiliary edge-prediction task which is designed to guide the model detection of the weak boundary of the hippocampus in model optimization. To balance the multiple task objectives, we proposed an improved gradient normalization by adaptively adjusting the weight of losses from different tasks. A total of 247 T1-weighted MRIs including 131 without contrast and 116 with contrast were collected from 247 patients to train and validate the proposed method. Segmentation was quantitatively evaluated with the dice coefficient (Dice), Hausdorff distance (HD), and average Hausdorff distance (AVD). The 3D U-net was used for baseline comparison. We used a Wilcoxon signed-rank test to compare repeated measurements (Dice, HD, and AVD) by different segmentations. Results: Through fivefold cross-validation, our multitask edge-aware learning achieved Dice of 0.8483 AE 0.0036, HD of 7.5706 AE 1.2330 mm, and AVD of 0.1522 AE 0.0165 mm, respectively. Conversely, the baseline results were 0.8340 AE 0.0072, 10.4631 AE 2.3736 mm, and 0.1884 AE 0.0286 mm, respectively. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P < 0.05). Conclusion:Our results demonstrated the efficiency of multitask edge-aware learning in hippocampus segmentation for hippocampal sparing whole-brain radiotherapy. The proposed framework may also be useful for other low-contrast small organ segmentations on medical imaging modalities.

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Section: Discussionmentioning

confidence: 99%

Automatic segmentation of hippocampus in hippocampal sparing whole brain radiotherapy: A multitask edge‐aware learning

Qiu

Yang

et al. 2021

Medical Physics

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“…Then, we trained ISN and TCN together with a learning rate of 0.0001 and a learning rate decay of 0.7 per epoch for another 10 epochs. Three loss functions including soft Dice loss, crossentropy loss, and contour loss (54) are used for training. The adaptive momentum estimation (ADAM) optimizer is adopted to update the parameters.…”

Section: Experiments Setupmentioning

confidence: 99%

Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis

Chen¹,

Xu²,

Zeng³

et al. 2022

Front. Cardiovasc. Med.

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Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.

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“…To address this problem, edge detection methods [38,39] and improved loss functions [40][41][42] have been proposed to overcome poor segmentation at the edges. The former adds a branch for edge detection in parallel with the existing branch for object mask predic-tion and uses edge prediction to strengthen the coarse segmentation results.…”

Section: D Object Segmentation and Edge Enhancementmentioning

confidence: 99%

Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)

et al. 2021

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Automated damage evaluation is of great importance in the maintenance and preservation of heritage structures. Damage investigation of large cultural buildings is time-consuming and labor-intensive, meaning that many buildings are not repaired in a timely manner. Additionally, some buildings in harsh environments are impossible to reach, increasing the difficulty of damage investigation. Oblique images facilitate damage detection in large buildings, yet quantitative damage information, such as area or volume, is difficult to generate. In this paper, we propose a method for quantitative damage evaluation of large heritage buildings in wild areas with repetitive structures based on drone images. Unlike existing methods that focus on building surfaces, we study the damage of building components and extract hidden linear symmetry information, which is useful for localizing missing parts in architectural restoration. First, we reconstruct a 3D mesh model based on the photogrammetric method using high-resolution oblique images captured by drone. Second, we extract 3D objects by applying advanced deep learning methods to the images and projecting the 2D object segmentation results to 3D mesh models. For accurate 2D object extraction, we propose an edge-enhanced method to improve the segmentation accuracy of object edges. 3D object fragments from multiple views are integrated to build complete individual objects according to the geometric features. Third, the damage condition of objects is estimated in 3D space by calculating the volume reduction. To obtain the damage condition of an entire building, we define the damage degree in three levels: no or slight damage, moderate damage and severe damage, and then collect statistics on the number of damaged objects at each level. Finally, through an analysis of the building structure, we extract the linear symmetry surface from the remaining damaged objects and use the symmetry surface to localize the positions of missing objects. This procedure was tested and validated in a case study (the Jiankou Great Wall in China). The experimental results show that in terms of segmentation accuracy, our method obtains results of 93.23% mAP and 84.21% mIoU on oblique images and 72.45% mIoU on the 3D mesh model. Moreover, the proposed method shows effectiveness in performing damage assessment of objects and missing part localization.

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Contour-Aware Loss: Boundary-Aware Learning for Salient Object Segmentation

Cited by 84 publications

References 47 publications

Automatic segmentation of hippocampus in hippocampal sparing whole brain radiotherapy: A multitask edge‐aware learning

Automatic segmentation of hippocampus in hippocampal sparing whole brain radiotherapy: A multitask edge‐aware learning

Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis

Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)

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