CVT-Vnet: a convolutional-transformer model for head and neck multi-organ segmentation

In this work, we propose a convolutional vision transformer V-net (CVT-Vnet) for multi-organ segmentation in 3-dimensional CT images of head and neck cancer patients for radiotherapy treatment planning. Organs include brain-stem, chiasm, mandible, optic nerve (left and right), parotid (left and right), and submandibular (left and right). The proposed CVT-Vnet has a U-shape encoder-decoder architecture. A CVT is firstly deployed as the encoder to encourage global characteristics which still preserve precise local details. And a convolutional decoder is utilized to assemble the segmentation from the features learned by the CVT. We evaluated the network using a dataset of 32 patients undergoing radiotherapy treatment. We present quantitative evaluation of the performance of our proposed CVT-Vnet, in terms of segmentation volume similarity (Dice score, sensitivity, precision and absolution percentage volume difference ( AVD)) and surface similarity (Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD)), using the physicians' manual contour as the ground truth. The volume similarities averaged over all organs were 0.79 as Dice score, 0.83 as sensitivity and 0.78 as precision. The average surface similarities were 13.41mm as HD, 0.39mm as MSD and 1.01mm as RMSD. The proposed network performed significantly better than Vnet and Deep-attention V-net, which are two state-of-the-art methods. The proposed CVT-Vnet can be a promising tool of multi-organ delineation for head and neck radiotherapy treatment planning.