Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation

Li, Caizi; Tong, Qianqian; Liao, Xiangyun; Si, Weixin; Sun, Yinzi; Wang, Qiong; Heng, Pheng‐Ann

doi:10.1007/978-3-030-12029-0_28

Cited by 27 publications

(20 citation statements)

References 8 publications

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“…To model long-range dependencies local and global features were combined in a simple attention module, which contains three convolutional layers followed by a softmax function to create the attention map. A similar attention module, composed of two convolutional layers followed by a softmax, was integrated in a hierarchical aggregation framework integrated in UNet for left atrial segmentation [24]. More recently, additive attention gate modules were integrated in the skip connections of the decoding path of UNet with the goal of better model complimentary information from the encoder [25].…”

Section: Medical Image Segmentation With Deep Attentionmentioning

confidence: 99%

“…Despite the growing interest on integrating attention mechanisms in image segmentation networks for natural scenes, their adoption in medical images remains scarce [23], [24], [25], [26], being limited to simple attention models. Thus, in this work, we explore more complex attention mechanisms that can boost the performance of standard deep networks for the task of medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Scale Self-Guided Attention for Medical Image Segmentation

Sinha

Dolz

2021

IEEE J. Biomed. Health Inform.

422

232

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Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple times at multiple scales. Second, long-range feature dependencies are not efficiently modeled, resulting in nonoptimal discriminative feature representations associated with each semantic class. In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms. This approach is able to integrate local features with their corresponding global dependencies, as well as highlight interdependent channel maps in an adaptive manner. Further, the additional loss between different modules guides the attention mechanisms to neglect irrelevant information and focus on more discriminant regions of the image by emphasizing relevant feature associations. We evaluate the proposed model in the context of abdominal organ segmentation on magnetic resonance imaging (MRI). A series of ablation experiments support the importance of these attention modules in the proposed architecture. In addition, compared to other state-of-the-art segmentation networks our model yields better segmentation performance, increasing the accuracy of the predictions while reducing the standard deviation. This demonstrates the efficiency of our approach to generate precise and reliable automatic segmentations of medical images. Our code and the trained model are made publicly available at: https://github.com/sinAshish/Multi-Scale-Attention

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Section: Medical Image Segmentation With Deep Attentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Scale Self-Guided Attention for Medical Image Segmentation

Sinha

Dolz

2021

IEEE J. Biomed. Health Inform.

422

232

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“…As a result, the learning process was entirely focused on a smaller region of interest (ROI), allowing better representation of the LA features. Based on a similar principle, other researchers (55)(56)(57), pushed this idea a step further by using a multi-CNN approach for atrial segmentation ( Figure 3A). In their approaches, two consecutive networks were employed instead.…”

Section: Multi-stage Cnn and Class Imbalancementioning

confidence: 99%

Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs

Jamart

Xiong

Talou

et al. 2020

Front. Cardiovasc. Med.

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Segmentation and 3D reconstruction of the human atria is of crucial importance for precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia. However, the current manual segmentation of the atria from medical images is a time-consuming, labor-intensive, and error-prone process. The recent emergence of artificial intelligence, particularly deep learning, provides an alternative solution to the traditional methods that fail to accurately segment atrial structures from clinical images. This has been illustrated during the recent 2018 Atrial Segmentation Challenge for which most of the challengers developed deep learning approaches for atrial segmentation, reaching high accuracy (>90% Dice score). However, as significant discrepancies exist between the approaches developed, many important questions remain unanswered, such as which deep learning architectures and methods to ensure reliability while achieving the best performance. In this paper, we conduct an in-depth review of the current state-of-the-art of deep learning approaches for atrial segmentation from late gadolinium-enhanced MRIs, and provide critical insights for overcoming the main hindrances faced in this task.

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“…Chen et al [ 6 ] introduced an attention-based approach to influence multi-scale features acquired at different scales for the segmentation of natural images and showed increased segmentation performance over conventional methods for predicting multi-scale features. Despite the integration of attention modules in natural image segmentation, their application to medical images is restricted to simple attention models [ 10 , 11 ]. In addition to accuracy, many embedded applications consider the model size, energy consumption, and inference time to be significant in real-time use.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation

Dayananda

Choi

Lee

2021

Sensors

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In this paper, we propose a multi-scale feature extraction with novel attention-based convolutional learning using the U-SegNet architecture to achieve segmentation of brain tissue from a magnetic resonance image (MRI). Although convolutional neural networks (CNNs) show enormous growth in medical image segmentation, there are some drawbacks with the conventional CNN models. In particular, the conventional use of encoder-decoder approaches leads to the extraction of similar low-level features multiple times, causing redundant use of information. Moreover, due to inefficient modeling of long-range dependencies, each semantic class is likely to be associated with non-accurate discriminative feature representations, resulting in low accuracy of segmentation. The proposed global attention module refines the feature extraction and improves the representational power of the convolutional neural network. Moreover, the attention-based multi-scale fusion strategy can integrate local features with their corresponding global dependencies. The integration of fire modules in both the encoder and decoder paths can significantly reduce the computational complexity owing to fewer model parameters. The proposed method was evaluated on publicly accessible datasets for brain tissue segmentation. The experimental results show that our proposed model achieves segmentation accuracies of 94.81% for cerebrospinal fluid (CSF), 95.54% for gray matter (GM), and 96.33% for white matter (WM) with a noticeably reduced number of learnable parameters. Our study shows better segmentation performance, improving the prediction accuracy by 2.5% in terms of dice similarity index while achieving a 4.5 times reduction in the number of learnable parameters compared to previously developed U-SegNet based segmentation approaches. This demonstrates that the proposed approach can achieve reliable and precise automatic segmentation of brain MRI images.

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Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation

Cited by 27 publications

References 8 publications

Multi-Scale Self-Guided Attention for Medical Image Segmentation

Multi-Scale Self-Guided Attention for Medical Image Segmentation

Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs

Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation

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