CAML-PSPNet: A Medical Image Segmentation Network Based on Coordinate Attention and a Mixed Loss Function

The problems of missed segmentation with fuzzy boundaries of segmented regions and small regions are common in segmentation tasks, and greatly decrease the accuracy of clinicians’ diagnosis. For this, a new network based on PSPNet, using a coordinate attention mechanism and a mixed loss function for...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 4; S. 1117
Hauptverfasser: Li, Yuxia, Li, Peng, Wang, Hailing, Gong, Xiaomei, Fang, Zhijun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.02.2025
MDPI
Schlagworte:
Accuracy
Algorithms
attention mechanism
Computer vision
Datasets
Deep learning
Humans
Image processing
Image Processing, Computer-Assisted - methods
image segmentation
Lung cancer
Lung Neoplasms - diagnostic imaging
lung tumor
Medical imaging equipment
Medical research
mixed loss
Neural Networks, Computer
PSPNet
Semantics
Tumors
Germany
PSPNet
lung tumor
mixed loss
attention mechanism
image segmentation
ISSN:1424-8220, 1424-8220
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Zusammenfassung:The problems of missed segmentation with fuzzy boundaries of segmented regions and small regions are common in segmentation tasks, and greatly decrease the accuracy of clinicians’ diagnosis. For this, a new network based on PSPNet, using a coordinate attention mechanism and a mixed loss function for segmentation (CAML-PSPNet), is proposed. Firstly, the coordinate attention module splits the input feature map into horizontal and vertical directions to locate the edge position of the segmentation target. Then, a Mixed Loss function (MLF) is introduced in the model training stage to solve the problem of the low accuracy of small-target tumor segmentation. Finally, the lightweight MobilenetV2 is utilized in backbone feature extraction, which largely reduces the model’s parameter count and enhances computation speed. Three datasets—PrivateLT, Kvasir-SEG and ISIC 2017—are selected for the experimental part, and the experimental results demonstrate significant enhancements in both visual effects and evaluation metrics for the segmentation achieved by CAML-PSPNet. Compared with Deeplabv3, HrNet, U-Net and PSPNet networks, the average intersection rates of CAML-PSPNet are increased by 2.84%, 3.1%, 5.4% and 3.08% on lung cancer data, 7.54%, 3.1%, 5.91% and 8.78% on Kvasir-SEG data, and 1.97%, 0.71%, 3.83% and 0.78% on ISIC 2017 data, respectively. When compared to other methods, CAML-PSPNet has the greatest similarity with the gold standard in boundary segmentation, and effectively enhances the segmentation accuracy for small targets.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s25041117