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SAA-SDM: Neural Networks Faster Learned to Segment Organ Images

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Abstract

In the field of medicine, rapidly and accurately segmenting organs in medical images is a crucial application of computer technology. This paper introduces a feature map module, Strength Attention Area Signed Distance Map (SAA-SDM), based on the principal component analysis (PCA) principle. The module is designed to accelerate neural networks’ convergence speed in rapidly achieving high precision. SAA-SDM provides the neural network with confidence information regarding the target and background, similar to the signed distance map (SDM), thereby enhancing the network’s understanding of semantic information related to the target. Furthermore, this paper presents a training scheme tailored for the module, aiming to achieve finer segmentation and improved generalization performance. Validation of our approach is carried out using TRUS and chest X-ray datasets. Experimental results demonstrate that our method significantly enhances neural networks’ convergence speed and precision. For instance, the convergence speed of UNet and UNET +  + is improved by more than 30%. Moreover, Segformer achieves an increase of over 6% and 3% in mIoU (mean Intersection over Union) on two test datasets without requiring pre-trained parameters. Our approach reduces the time and resource costs associated with training neural networks for organ segmentation tasks while effectively guiding the network to achieve meaningful learning even without pre-trained parameters. 

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Data Availability

Published public datasets were used for our experiments.

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Authors and Affiliations

  1. College of Computer and Information Technology, China Three Gorges University, Yichang Hubei, 443002, China

    Chao Gao, Yongtao Shi, Shuai Yang & Bangjun Lei

  2. Hubei Key Laboratory of Intelligent Vision Monitoring for Hydropower Engineering, China Three Gorges University, Yichang Hubei, 443002, China

    Chao Gao, Yongtao Shi, Shuai Yang & Bangjun Lei

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  1. Chao Gao
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  2. Yongtao Shi
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Contributions

Conceptualization, Y.S. and C.G.; methodology, C.G.; software, S.Y and C.G..; validation, C.G., R.Z, S.Y. and B.L.; formal analysis, C.G.; investigation, S.Y. and C.G.; data curation, Y.S.; writing—original draft preparation, S.Y. and C.G..; writing—review and editing, C.G.; visualization, S.Y. and C.G.; supervision, Y.S. and B.L.; project administration, Y.S. and B.L. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yongtao Shi.

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Gao, C., Shi, Y., Yang, S. et al. SAA-SDM: Neural Networks Faster Learned to Segment Organ Images. J Digit Imaging. Inform. med. 37, 547–562 (2024). https://doi.org/10.1007/s10278-023-00947-1

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  • DOI: https://doi.org/10.1007/s10278-023-00947-1

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