Automatic cervical lymphadenopathy segmentation from CT data using deep learning

Abstract

Purpose: The purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination.

Materials and methods: An ensemble of three convolutional neural networks (CNNs) based on a U-Net architecture were trained to segment the lymphadenopathies in a fully supervised framework. The resulting predictions were assessed using the Dice similarity coefficient (DSC) on examinations presenting one or more adenopathies. On examinations without adenopathies, the score was given by the formula M/(M+A) where M was the mean adenopathy volume per patient and A the volume segmented by the algorithm. The networks were trained on 117 annotated CT acquisitions.

Results: The test set included 150 additional CT acquisitions unseen during the training. The performance on the test set yielded a mean score of 0.63.

Conclusion: Despite limited available data and partial annotations, our CNN based approach achieved promising results in the task of cervical lymphadenopathy segmentation. It has the potential to bring precise quantification to the clinical workflow and to assist the clinician in the detection task.

Keywords: Artificial intelligence; Computer-assisted; Deep learning; Image processing; Lymphadenopathy; Tomography; X-ray computed.