DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images

Abstract

Automatic lymphoma detection and accurate lymphoma boundary delineation from whole body Positron Emission Tomography/Computed Tomography (PET/CT) scans are essential for surgical navigation and radiation therapy. Besides, labeling the data, which means contouring the lymphoma contour in images is time-consuming, operator intensive and subjective. Hence, this paper integrates the supervised learning and unsupervised learning to propose an end-to-end segmentation network, namely DenseX-Net, for both lymphoma detection and segmentation. There are two important flows in the proposed DenseX-Net. One is a reconstruction flow (based on the convolutional encoder-decoder form) that can be used for learning semantic representations of different lymphomas by minimizing the discrepancy between each input and its output in an unsupervised learning form. The other one is a segmentation flow (based on DenseU-Net) that performs the lymphoma segmentation task. Note that, the encoders in both flows are trained jointly with the same weights, which can facilitate DenseX-Net obtaining the accurate segmentation using a little labeled data. We evaluate our proposed DenseX-Net for lymphoma segmentation on 80 real PET/CT cases (from General Hospital of Northern Military Area) with a Dice coefficient of 72.84%. Experimentations and comparisons demonstrate the accuracy and robustness of DenseX-Net as well as its performance advantages as compared with related segmentation networks.

Keywords

• Lymphoma segmentation
• deep learning
• PET/CT
• semi-supervised learning
• computer aided diagnosis