Disease Quantification of Liver Lymphoma in CT Images without Lesion Segmentation

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.

Keywords: Convolutional neutral network; Deep learning; Disease map; Disease quantification; Image segmentation; Liver lymphoma; U-Net.