Machine learning and image analysis in vascular surgery.

Journal: Seminars in vascular surgery
Published Date:

Abstract

Deep learning, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, deep learning methods learn image features and classify data without making prior assumptions. Convolutional neural networks, the main type of deep learning for computer vision processing, are neural networks with multilevel architecture and weighted connections between nodes that can "auto-learn" through repeated exposure to training data without manual input or supervision. These networks have numerous applications in vascular surgery imaging analysis, particularly in disease classification, object identification, semantic segmentation, and instance segmentation. The purpose of this review article was to review the relevant concepts of machine learning image analysis and its application to the field of vascular surgery.

Authors

  • Roger T Tomihama
    Department of Radiology, Section of Vascular and Interventional Radiology, Linda University School of Medicine, 11234 Anderson Street, Suite MC-2605E, Loma Linda, CA 92354. Electronic address: roger.tomihama@gmail.com.
  • Saharsh Dass
    Department of Radiology, Section of Vascular and Interventional Radiology, Linda University School of Medicine, 11234 Anderson Street, Suite MC-2605E, Loma Linda, CA 92354.
  • Sally Chen
    Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, Loma Linda, CA.
  • Sharon C Kiang
    Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, Loma Linda, CA; Department of Surgery, Division of Vascular Surgery, Veterans Affairs Loma Linda Healthcare System, Loma Linda, CA.