Prostate and tumor segmentation on PET/CT using Dual Mask R-CNN

Tonghe Wang; Yang Lei; Oladunni O. Akin-Akintayo; Olayinka A. Abiodun Ojo; Akinyemi A. Akintayo; Walter J. Curran; Tian Liu; David M. Schuster; Xiaofeng Yang

doi:10.1117/12.2580970

15 February 2021 Prostate and tumor segmentation on PET/CT using Dual Mask R-CNN

Tonghe Wang, Yang Lei, Oladunni O. Akin-Akintayo, Olayinka A. Abiodun Ojo, Akinyemi A. Akintayo, Walter J. Curran, Tian Liu, David M. Schuster, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116000S (2021) https://doi.org/10.1117/12.2580970
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

We propose a learning-based method to automatically segment prostate and its dominant intraprostatic lesions (DILs) from positron emission tomography (PET)/computed tomography (CT) images. A dual attention mask R-CNN is introduced to enable end-to-end segmentation. To avoid the effect of useless region, mask R-CNN is used to get rid of non-tumor regions via fist locate the tumor region-of-interest (ROI) and then segment tumor within that ROI. Dual attention networks are used as backbone in mask R-CNN to extract comprehensive features from both CT and PET images. The binary mask of tumor of an arrival patient’s CT and PET image is generated by the well-trained network. To evaluate the proposed method, we retrospectively investigate 25 PET/CT datasets. On each dataset, prostate and DILs were delineated by physicians and was served as ground truth and training target. The proposed method was trained and evaluated by a Five-fold cross validation strategy. The average centroid distance, volume difference and DSC value for prostate/DIL among all 25 patients are 0.83±0.91mm, -0.01±0.79 and 0.84±0.09, respectively. The proposed method has great potential in improving the efficiency and mitigating the observer-dependence in prostate and DIL contouring for DIL focal boost radiation therapy

Conference Presentation

Citation Download Citation

Tonghe Wang, Yang Lei, Oladunni O. Akin-Akintayo, Olayinka A. Abiodun Ojo, Akinyemi A. Akintayo, Walter J. Curran, Tian Liu, David M. Schuster, and Xiaofeng Yang "Prostate and tumor segmentation on PET/CT using Dual Mask R-CNN", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116000S (15 February 2021); https://doi.org/10.1117/12.2580970

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