Contour-aware semantic segmentation network with spatial attention mechanism for medical image

Cheng, Zhiming; Qu, Ang; He, Xiaofeng

doi:10.1007/s00371-021-02075-9

Cited by 42 publications

(11 citation statements)

References 40 publications

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“…Additionally, the ResNet used in the FPN structure circumvents the gradient explosion, causing FPN to effectively learn the seagrass features even in deeper networks and to yield better classification results [60]. The worse results of the U-Net might be attributable to its learning for redundant features and ignoring of small objects [27,61]. Nevertheless, unsuccessful classification pixels existed in areas where a large brightness gradient appeared (e.g., sun glint, see Figure 13C) or the texture information in non-seagrass areas was similar to that in seagrass areas (Figure 13D).…”

Section: Discussionmentioning

confidence: 99%

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Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos

Chen

Sasaki

2021

Remote Sensing

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Seagrass meadows are one of the blue carbon ecosystems that continue to decline worldwide. Frequent mapping is essential to monitor seagrass meadows for understanding change processes including seasonal variations and influences of meteorological and oceanic events such as typhoons and cyclones. Such mapping approaches may also enhance seagrass blue carbon strategy and management practices. Although unmanned aerial vehicle (UAV) aerial photography has been widely conducted for this purpose, there have been challenges in mapping accuracy, efficiency, and applicability to subtidal water meadows. In this study, a novel method was developed for mapping subtidal and intertidal seagrass meadows to overcome such challenges. Ground truth seagrass orthophotos in four seasons were created from the Futtsu tidal flat of Tokyo Bay, Japan, using vertical and oblique UAV photography. The feature pyramid network (FPN) was first applied for automated seagrass classification by adjusting the spatial resolution and normalization parameters and by considering the combinations of seasonal input data sets. The FPN classification results ensured high performance with the validation metrics of 0.957 overall accuracy (OA), 0.895 precision, 0.942 recall, 0.918 F1-score, and 0.848 IoU, which outperformed the conventional U-Net results. The FPN classification results highlighted seasonal variations in seagrass meadows, exhibiting an extension from winter to summer and demonstrating a decline from summer to autumn. Recovery of the meadows was also detected after the occurrence of Typhoon No. 19 in October 2019, a phenomenon which mainly happened before summer 2020.

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Section: Discussionmentioning

confidence: 99%

“…Although their results were accurate for intertidal waters, their applicability to subtidal water seagrass meadows posed challenges. U-Net is also unsuitable for classifying small seagrass objects in the image [27]. The feature pyramid network (FPN) was first introduced for object detection.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos

Chen

Sasaki

2021

Remote Sensing

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“…It demonstrates an innovative regularization scheme on the attention weight mask, allowing the network to focus on lesions while allowing it to search in different regions. Cheng et al [45] proposed a contouraware semantic segmentation network including a semantic branch and a detail branch. Moreover, in order to improve the representation ability of the network, the author uses the Mul-Block module to extract semantic information with different receptive fields, and the spatial attention module is used to adaptively suppress redundant features.…”

Section: H Wmentioning

confidence: 99%

Image segmentation technology based on transformer in medical decision‐making system

Gou

2023

IET Image Processing

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Due to the improvement in computing power and the development of computer technology, deep learning has pene‐trated into various fields of the medical industry. Segmenting lesion areas in medical scans can help clinicians make accurate diagnoses. In particular, convolutional neural networks (CNNs) are a dominant tool in computer vision tasks. They can accurately locate and classify lesion areas. However, due to their inherent inductive bias, CNNs may lack an understanding of long‐term dependencies in medical images, leading to less accurate grasping of details in the images. To address this problem, we explored a Transformer‐based solution and studied its feasibility in medical imaging tasks (OstT). First, we performed super‐resolution reconstruction on the original MRI image of osteosarcoma and improved the texture features of the tissue structure to reduce the error caused by the unclear tissue structure in the image during model training. Then, we propose a Transformer‐based method for medical image segmentation. A gated axial attention model is used, which augments existing architectures by introducing an additional control mechanism in the self‐attention module to improve segmentation accuracy. Experiments on real datasets show that our method outper‐forms existing models such as Unet. It can effectively assist doctors in imaging examinations.

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“…This includes two branches known as Semantic and Detail branches that extract information from the Deep and Shallow layers. A Malblock module is used that capitalizes on the idea of collective knowledge and Class Activation MAP (CAM) to reduce the redundant feature [24]. Another modification in U-Net is made on paper [25].…”

Section: Related Workmentioning

confidence: 99%

A Convolutional Neural Network for Skin Lesion Segmentation Using Double U-Net Architecture

Almakdi¹,

Almulihi²,

Alqahtani³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Skin lesion segmentation plays a critical role in the precise and early detection of skin cancer via recent frameworks. The prerequisite for any computer-aided skin cancer diagnosis system is the accurate segmentation of skin malignancy. To achieve this, a specialized skin image analysis technique must be used for the separation of cancerous parts from important healthy skin. This procedure is called Dermatography. Researchers have often used multiple techniques for the analysis of skin images, but, because of their low accuracy, most of these methods have turned out to be at best, inconsistent. Proper clinical treatment involves sensitivity in the surgical process. A high accuracy rate is therefore of paramount importance. A generalized and robust model is needed to accurately assess and segment skin lesions. In this regard, a novel approach named Double U-Net has been proposed to provide necessary strength and Robustness. This process uses two U-Net architectures stacked upon each other with ASPP which is used to squeeze out a high resolution and redundant information. In this paper, we trained the proposed architecture on the PH 2 dataset and the model was evaluated on the PH 2 test, ISIC-2016 and HAM datasets. Evaluation of information shows the model achieved a DSC of 0.9551 on the PH 2 test dataset, 0.8104 on ISIC-2016 and 0.7645 on the HAM dataset. Analyses show results comparable to the most recently available state-of-the-art techniques.

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Contour-aware semantic segmentation network with spatial attention mechanism for medical image

Cited by 42 publications

References 40 publications

Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos

Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos

Image segmentation technology based on transformer in medical decision‐making system

A Convolutional Neural Network for Skin Lesion Segmentation Using Double U-Net Architecture

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