Anatomy-guided deep learning for object localization in medical images

Chao Jin; Jayaram Udupa; Liming Zhao; Yubing Tong; Dewey Odhner; Gargi Pednekar; Sanghita Nag; Sharon Lewis; Nicholas Poole; Sutirth Mannikeri; Sudarshana Govindasamy; Aarushi Singh; Joe Camaratta; Steve Owens; Drew Torigian

doi:10.1117/12.2612566

4 April 2022 Anatomy-guided deep learning for object localization in medical images

Chao Jin, Jayaram Udupa, Liming Zhao, Yubing Tong, Dewey Odhner, Gargi Pednekar, Sanghita Nag, Sharon Lewis, Nicholas Poole, Sutirth Mannikeri, Sudarshana Govindasamy, Aarushi Singh, Joe Camaratta, Steve Owens, Drew Torigian

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203228 (2022) https://doi.org/10.1117/12.2612566
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Image segmentation is the process of delineating the regions occupied by objects of interest in a given image. This operation is a fundamentally required first step in numerous applications of medical imagery. In the medical imaging field, this activity has a rich literature that spans over 45 years. In spite of numerous advances, including deep learning (DL) networks (DLNs) in recent years, the problem has defied a robust, fail-safe, and satisfactory solution, especially for objects that are manifest with low contrast, are spatially sparse, have variable shape among individuals, or are affected by imaging artifacts, pathology, or post-treatment change in the body. Although image processing techniques, notably DLNs, are uncanny in their ability to harness low-level intensity pattern information on objects, they fall short in the high-level task of identifying and localizing an entire object as a gestalt. This dilemma has been a fundamental unmet challenge in medical image segmentation. In this paper, we demonstrate that by synergistically marrying the unmatched strengths of high-level human knowledge (i.e., natural intelligence (NI)) with the capabilities of DL networks (i.e., artificial intelligence (AI)) in garnering intricate details, these challenges can be significantly overcome. Focusing on the object recognition task, we formulate an anatomy-guided DL object recognition approach named Automatic Anatomy Recognition-Deep Learning (AAR-DL) which combines an advanced anatomy-modeling strategy, model-based non-DL object recognition, and DL object detection networks to achieve expert human-like performance.

Citation Download Citation

Chao Jin, Jayaram Udupa, Liming Zhao, Yubing Tong, Dewey Odhner, Gargi Pednekar, Sanghita Nag, Sharon Lewis, Nicholas Poole, Sutirth Mannikeri, Sudarshana Govindasamy, Aarushi Singh, Joe Camaratta, Steve Owens, and Drew Torigian "Anatomy-guided deep learning for object localization in medical images", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203228 (4 April 2022); https://doi.org/10.1117/12.2612566

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available