Related Experiment Video
Updated: Nov 20, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
DiSegNet: A deep dilated convolutional encoder-decoder architecture for lymph node segmentation on PET/CT images
Guoping Xu1, Hanqiang Cao2, Jayaram K Udupa3
1School of Computer Sciences and Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China; Medical Image Processing Group, 602 Goddard Building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Purpose:
Automated lymph node (LN) recognition and segmentation from cross-sectional medical images is an important step for the automated diagnostic assessment of patients with cancer. Yet, it is still a difficult task owing to the low contrast of LNs and surrounding soft tissues as well as due to the variation in nodal size and shape. In this paper, we present a novel LN segmentation method based on a newly designed neural network for positron emission tomography/computed tomography (PET/CT) images.
Methods:
This work communicates two problems involved in LN segmentation task. Firstly, an efficient loss function named cosine-sine (CS) is proposed for the voxel class imbalance problem in the convolution network training process. Second, a multi-stage and multi-scale Atrous (Dilated) spatial pyramid pooling sub-module, named MS-ASPP, is introduced to the encoder-decoder architecture (SegNet), which aims to make use of multi-scale information to improve the performance of LN segmentation. The new architecture is named DiSegNet (Dilated SegNet).
Results:
Four-fold cross-validation is performed on 63 PET/CT data sets. In each experiment, 10 data sets are selected randomly for testing and the other 53 for training. The results show that we reach an average 77 % Dice similarity coefficient score with CS loss function by trained DiSegNet, compared to a baseline method SegNet by cross-entropy (CE) with 71 % Dice similarity coefficient.
Conclusions:
The performance of the proposed DiSegNet with CS loss function suggests its potential clinical value for disease quantification.
