AATSN: Anatomy Aware Tumor Segmentation Network for PET-CT volumes and images using a lightweight fusion-attention mechanism

Ahmad, Ibtihaj; Xia, Yong; Cui, Hengfei; Islam, Zain Ul

doi:10.1016/j.compbiomed.2023.106748

Cited by 14 publications

(3 citation statements)

References 25 publications

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“…The modality-specific features are then aggregated using 1×1×1 convolution and sent to the decoder for final segmentation. Ahmad et al ( 101 ) used the attention mechanism to fuse PET modal data features in the decoding part after extracting features from CT, which improved the segmentation performance of the network. Zhu et al ( 102 ) used the cruciform structure extracted from PET images as additional information.…”

Section: Discussionmentioning

confidence: 99%

Multi-modal tumor segmentation methods based on deep learning: a narrative review

Xue,

Yao,

Teng

2024

Quant Imaging Med Surg

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Background and Objective Automatic tumor segmentation is a critical component in clinical diagnosis and treatment. Although single-modal imaging provides useful information, multi-modal imaging provides a more comprehensive understanding of the tumor. Multi-modal tumor segmentation has been an essential topic in medical image processing. With the remarkable performance of deep learning (DL) methods in medical image analysis, multi-modal tumor segmentation based on DL has attracted significant attention. This study aimed to provide an overview of recent DL-based multi-modal tumor segmentation methods. Methods In in the PubMed and Google Scholar databases, the keywords “multi-modal”, “deep learning”, and “tumor segmentation” were used to systematically search English articles in the past 5 years. The date range was from 1 January 2018 to 1 June 2023. A total of 78 English articles were reviewed. Key Content and Findings We introduce public datasets, evaluation methods, and multi-modal data processing. We also summarize common DL network structures, techniques, and multi-modal image fusion methods used in different tumor segmentation tasks. Finally, we conclude this study by presenting perspectives for future research. Conclusions In multi-modal tumor segmentation tasks, DL technique is a powerful method. With the fusion methods of different modal data, the DL framework can effectively use the characteristics of different modal data to improve the accuracy of tumor segmentation.

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Section: Discussionmentioning

confidence: 99%

Multi-modal tumor segmentation methods based on deep learning: a narrative review

Xue,

Yao,

Teng

2024

Quant Imaging Med Surg

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“…Attentional mechanisms have gained extensive application in the domain of medical image segmentation. Ahmad et al [36] introduces a nimble fusion-attentional decoder mechanism, augmenting the precision of tumor segmentation. Li et al [37] employs an attentional mechanism to identify global contextual information in three dimensions simultaneously: the channel domain, the spatial domain, and the feature internal domain, aiming to capture more representative features.…”

Section: Attention-based Medical Image Segmentationmentioning

confidence: 99%

Cobb Angle Measurement Based on Spine Segmentation Using ATT UNet 3+

Peng,

Hu,

Zhang

et al. 2024

IEEE Access

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Scoliosis refers to the abnormal curvature of human spine, which is one of the most common deformities in children and adolescents. The Cobb angle is the gold standard for quantifying the severity of scoliosis and is used to assess the severity of scoliosis. Often the accuracy of the Cobb angle measurement relies on the subjective experience of the doctor and the process is very time consuming. In this study, we propose a new deep neural network, ATT UNet 3+, based on UNet 3+. Our approach incorporates a novel hybrid attention mechanism in the network's upsampling process. This mechanism allows for the appropriate reweighting of fused multi-scale information and facilitates effective supervision of the final output results. The proposed neural network is trained, tested and validated on 155 X-ray ortho-slices. The deep learning network is compared with the more effective neural networks commonly used today. ATT UNet 3+ achieves the best performance in the segmentation evaluation results. Regarding the final Cobb angle calculations, the absolute mean error between the longest distance ellipsoidal point (LDEP) method and expert measurements amounted to 1.6°. ATT UNet 3+ provides a potential tool for segmenting the spine in X-ray, which can improve the efficiency and accuracy of doctors in processing scoliosis pathological images.

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“…For semantic segmentation and edge proposal, we retain our previous loss function [27,28], since it has proven to be the most productive for semantic segmentation. This loss function is represented by 9, l a = Dice Loss * Jaccard Loss Dice Loss + Jaccard Loss…”

Section: Loss Functionmentioning

confidence: 99%

DAN-NucNet++: A Bottom-Up Framework Using Dual Attention Network for Simultaneous Nuclei Segmentation and Classification in Digital Histology Images

et al. 2022

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AATSN: Anatomy Aware Tumor Segmentation Network for PET-CT volumes and images using a lightweight fusion-attention mechanism

Cited by 14 publications

References 25 publications

Multi-modal tumor segmentation methods based on deep learning: a narrative review

Multi-modal tumor segmentation methods based on deep learning: a narrative review

Cobb Angle Measurement Based on Spine Segmentation Using ATT UNet 3+

DAN-NucNet++: A Bottom-Up Framework Using Dual Attention Network for Simultaneous Nuclei Segmentation and Classification in Digital Histology Images

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