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A novel solution of using deep learning for prostate cancer segmentation: enhanced batch normalization

机译：使用深度学习前列腺癌细分的新解决方案：增强批量标准化

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摘要

Deep learning has not been successfully implemented in the past with accurate segmentation of prostate on Magnetic Resonance (MR) image in nerve sparing prostate surgery. This was mainly due to the disease-specific change in shape, boundary of gland, and complexity in separating surrounding tissues. This research aims for accurate segmentation of prostate on MR images by combining multi-level features for decreasing the processing time of the process in prostate surgery. The proposed system consists of a deep neural network that extracts high-level and low-level features of MR images, and a propagation technique that combines the extracted features thus increasing the segmentation accuracy and reducing the time required for segmentation process. Accuracy is calculated using dice similarity coefficient and performance is calculated with total execution time of the datasets. The results show improved performance with 2.11 s against 2.29 s. In addition, the overall accuracy is improved to 95.3%% against 92.76% for the MR prostate segmentation. The proposed system focuses on automating the prostate segmentation in MR images with enhanced accuracy, and thus assisting prostate surgeries and disease diagnosis. This study solves the issues related to prostate shape recognition and prostate localization and improves the segmentation accuracy and performance.
机译：过去，深入学习尚未成功地实施前列腺术前列腺手术中磁共振（MR）图像的前列腺精确分割。这主要是由于疾病特异性变化，腺体边界和分离周围组织的复杂性。该研究旨在通过组合多级特征来降低前列腺手术中工艺的处理时间来精确分割MR图像。所提出的系统包括深度神经网络，提取MR图像的高级和低级特征，以及组合提取的特征的传播技术，从而增加了分割精度并减少了分割过程所需的时间。使用骰子相似度系数计算精度，并且使用数据集的总执行时间计算性能。结果表明，对2.29秒的2.11秒表示改善的性能。此外，总体准确性提高至92.76％的95.3％，对于先生前列区细分。该建议的系统侧重于将MR图像中的前列腺分段进行了增强的准确性，从而协助前列腺手术和疾病诊断。本研究解决了与前列腺形状识别和前列腺定位有关的问题，并提高了分割准确性和性能。

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  • 来源
    《Multimedia Tools and Applications》 |2021年第14期|21293-21313|共21页
  • 作者

    Shrestha Sushma; Alsadoon Abeer; Prasad P. W. C.; Seher Indra; Alsadoon Omar Hisham;

  • 作者单位

    Charles Sturt Univ CSU Sch Comp & Math Wagga Wagga NSW Australia;

    Charles Sturt Univ CSU Sch Comp & Math Wagga Wagga NSW Australia|Univ Western Sydney UWS Sch Comp Data & Math Sci Sydney NSW Australia|Southern Cross Univ SCU Sch Informat Technol Sydney NSW Iraq|Asia Pacific Int Coll APIC Informat Technol Dept Sydney NSW Australia|Kent Inst Australia Informat Technol Dept Sydney NSW Australia;

    Charles Sturt Univ CSU Sch Comp & Math Wagga Wagga NSW Australia;

    Charles Sturt Univ CSU Sch Comp & Math Wagga Wagga NSW Australia|Univ Western Sydney UWS Sch Comp Data & Math Sci Sydney NSW Australia;

    Al Iraqia Univ Dept Islamic Sci Baghdad Iraq;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Automatic prostate segmentation; Deep neural network; Magnetic resonance (MR) images; Prostate surgery;

    机译：自动前列腺分割;深神经网络;磁共振（MR）图像;前列腺手术;

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