计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220200063-10.doi: 10.11896/jsjkx.220200063

• 图像处理&多媒体技术 • 上一篇    下一篇

CT影像阶段化目标检测方法研究

王笑天1, 李博2, 康晓东1, 刘汉卿1, 韩俊玲1, 杨靖怡1   

  1. 1 天津医科大学医学影像学院 天津 300202;
    2 天津医科大学三中心临床学院 天津 300170
  • 出版日期:2023-06-10 发布日期:2023-06-12
  • 通讯作者: 康晓东(423065302@qq.com)
  • 作者简介:(kbwangedu@163.com)
  • 基金资助:
    京津冀协同创新项目(17YEXTZC00020)

Study on Phased Target Detection in CT Image

WANG Xiaotian1, LI Bo2, KANG Xiaodong1, LIU Hanqing1, HAN Junling1, YANG Jingyi1   

  1. 1 School of Medical Image,Tianjin Medical University,Tianjin 300202,China;
    2 The Third Central Clinical College of Tianjin Medical University,Tianjin 300170,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:WANG Xiaotian,born in 1998,undergraduate.His main research interests include medical image processing and so on. KANG Xiaodong,born in 1964,Ph.D,professor.His main research interests include medical image processing and medical information system integration.
  • Supported by:
    Beijing-Tianjin-Hebei Collaborative Innovation Proejct(17YEXTZC00020).

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摘要： CT是临床最常用的影像学检查之一,CT影像的计算机辅助诊断技术具有重要的临床意义。为优化CT影像目标检测,分别采用8种不同目标检测算法对肝血管瘤增强CT的图像、脑动脉狭窄CTA图像和结肠息肉CT图像进行检测研究,比较不同算法的适用性。首先,对肝血管瘤增强CT图像、脑动脉狭窄CTA和结肠息肉CT图像进行标注并制作数据集。其次,采用不同参数优化算法,并绘制AP-epoch和AP-FPS曲线以比较不同算法的检测性能。实验结果表明,PPYOLOv2在不同数据集中的AP,AP50,AP75和Recall均达到最优,预测边界框紧贴待检目标,预测置信度较高,并且具有良好的泛化能力和鲁棒性。

关键词: 目标检测, 深度学习算法, CT, CTA

Abstract: CT is one of the most commonly used imaging examinations in clinic,and the computer-aided diagnosis of CT images has important clinical significance.In order to optimize target detection in CT images,eight different target detection algorithms are used to detect hepatic hemangioma enhanced CT images,cerebral artery stenosis CTA images and colonic polyp CT images,and the applicability of different algorithms are compared.Firstly,the enhanced CT images of hepatic hemangioma,CTA images of cerebral artery stenosis and CT images of colonic polyps are labeled and datasets are made.Secondly,different parameter optimization algorithms are used,and AP-epoch and AP-FPS curves are drawn to compare the detection performance of different algorithms.Experimental results show that the AP,AP50,AP75 and Recall of PPYOLOv2 are optimal in different data sets,the prediction boundary box is close to the target to be tested,the prediction confidence is high,and it has good generalization ability and robustness.

Key words: Target detection, Deep learning algorithm, CT, CTA

中图分类号: 

  • TP391
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