Abstract
Adaptive radiotherapy (ART) for head and neck cancer (HNC) relies on repeated cone-beam CT (CBCT) imaging to monitor anatomical changes throughout treatment course. Accurate organ-at-risk (OAR) segmentation on these images is essential for dose monitoring, yet most existing CBCT segmentation methods process each fraction independently, ignoring temporal anatomical changes. We propose a longitudinal ConvLSTM-UNet for multi-organ segmentation of CBCT-derived synthetic CT (sCT) images. The framework was evaluated under two clinically relevant settings: fully automatic autoregressive inference and replanning-guided inference that updates temporal memory using contours from replanning CTs. Compared with a nnU-Net baseline, replanning-guided inference achieved the best overall performance (mean DSC 77.4%, mean ASD 1.67 mm), significantly improving 12 of 17 OARs, while autoregressive inference remained competitive without manual updates. We further performed a longitudinal dosimetric analysis that revealed significant dose increases in the parotid and submandibular glands. 46% of patients exceeded the 26 Gy contralateral parotid mean-dose constraint during treatment despite remaining below it on the planning CT based on rigid dose propagation. These results demonstrate that longitudinal deep learning improves multi-organ CBCT segmentation while enabling automated dosimetric monitoring, supporting its potential for ART workflows.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_031.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{CubLuc_CBCT_MICCAISAT2026,
author = { Cubero, Lucía AND Navarro Alfaro, Elena AND Rakotoarisedy, Irina AND Barateau, Anaïs AND Castelli, Joël AND de Crevoisier, Renaud AND Acosta, Oscar AND Pascau, Javier},
title = { { CBCT Segmentation in Head and Neck ART: A Longitudinal Deep Learning Approach } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}