Title:Disease Quantification of Liver Lymphoma in CT Images without Lesion
Segmentation
Volume: 20
Author(s): Kexin Li*, Xinwang Huang, Chunxue Sun, Qiancheng Xie and Shijie Cong
Affiliation:
- Department of Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi 214028, China
Keywords:
Disease quantification, Liver lymphoma, Image segmentation, Deep learning, U-Net, Convolutional neutral network, Disease map.
Abstract:
Aim:
This study aimed to automatically implement liver disease quantification (DQ) in lymphoma using CT images without lesion segmentation.
Background:
Computed Tomography (CT) imaging manifestations of liver lymphoma include diffuse infiltration, blurred boundaries, vascular drift signs, and
multiple lesions, making liver lymphoma segmentation extremely challenging.
Methods:
The method includes two steps: liver recognition and liver disease quantification. We use the transfer learning technique to recognize the diseased
livers automatically and delineate the livers manually using the CAVASS software. When the liver is recognized, liver disease quantification is
performed using the disease map model. We test our method in 10 patients with liver lymphoma. A random grouping cross-validation strategy is
used to evaluate the quantification accuracy of the manual and automatic methods, with reference to the ground truth.
Results:
We split the 10 subjects into two groups based on lesion size. The average accuracy for the total lesion burden (TLB) quantification is 91.76% ±
0.093 for the group with large lesions and 95.57% ± 0.032 for the group with small lesions using the manual organ (MO) method. An accuracy of
85.44% ± 0.146 for the group with larger lesions and 81.94% ± 0.206 for the small lesion group is obtained using the automatic organ (AO)
method, with reference to the ground truth.
Conclusion:
Our DQ-MO and DQ-AO methods show good performance for varied lymphoma morphologies, from homogeneous to heterogeneous, and from
single to multiple lesions in one subject. Our method can also be extended to CT images of other organs in the abdomen for disease quantification,
such as Kidney, Spleen and Gallbladder.
