Higher accuracy and lower complexity: convolutional neural network for multi-organ segmentation

Jingyi Song; Zhiqiang Tian; Chengyang Zhang; Yaoyue Zheng; Xiaofu Yu; Zhong Shi

doi:10.1117/12.2577009

12 October 2020 Higher accuracy and lower complexity: convolutional neural network for multi-organ segmentation

Jingyi Song, Zhiqiang Tian, Chengyang Zhang, Yaoyue Zheng, Xiaofu Yu, Zhong Shi

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 1157408 (2020) https://doi.org/10.1117/12.2577009
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

In computed tomography (CT), segmentation of organs-at-risk (OARs) is a key task in formulating the radiation therapy (RT) plan. However, it takes a lot of time to delineate OARs slice by slice in CT scans. The proposal of deep convolutional neural networks makes it possible to effectively segment medical images automatically. In this work, we propose an improved 2D U-Net to segment multiple OARs, aiming to increase accuracy while reducing complexity. Our method replaces vanilla convolutions with Octave Convolution (OctConv) units to reduce memory use and computation cost without accuracy sacrifice. We further plug a ‘Selective Kernel’ (SK) block after the encoder to capture multi-scale information and adaptively recalibrate the learned feature maps with attention mechanism. An in-house dataset is used to evaluate our method, where four chest organs are involved: left lung, right lung, heart, and spinal cord. Compared with the naive U-Net, the proposed method can improve Dice by up to nearly 3% and has fewer float-point operations (FLOPs).

Citation Download Citation

Jingyi Song, Zhiqiang Tian, Chengyang Zhang, Yaoyue Zheng, Xiaofu Yu, and Zhong Shi "Higher accuracy and lower complexity: convolutional neural network for multi-organ segmentation", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 1157408 (12 October 2020); https://doi.org/10.1117/12.2577009

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