TY - GEN

T1 - Multimodal CT and MR Segmentation of Head and Neck Organs-at-Risk

AU - Podobnik, Gašper

AU - Strojan, Primož

AU - Peterlin, Primož

AU - Ibragimov, Bulat

AU - Vrtovec, Tomaž

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023

Y1 - 2023

N2 - Radiotherapy (RT) is a standard treatment modality for head and neck (HaN) cancer that requires accurate segmentation of target volumes and nearby healthy organs-at-risk (OARs) to optimize radiation dose distribution. However, computed tomography (CT) imaging has low image contrast for soft tissues, making accurate segmentation of soft tissue OARs challenging. Therefore, magnetic resonance (MR) imaging has been recommended to enhance the segmentation of soft tissue OARs in the HaN region. Based on our two empirical observations that deformable registration of CT and MR images of the same patient is inherently imperfect and that concatenating such images at the input layer of a deep learning network cannot optimally exploit the information provided by the MR modality, we propose a novel modality fusion module (MFM) that learns to spatially align MR-based feature maps before fusing them with CT-based feature maps. The proposed MFM can be easily implemented into any existing multimodal backbone network. Our implementation within the nnU-Net framework shows promising results on a dataset of CT and MR image pairs from the same patients. Furthermore, the evaluation on a clinically realistic scenario with the missing MR modality shows that MFM outperforms other state-of-the-art multimodal approaches.

AB - Radiotherapy (RT) is a standard treatment modality for head and neck (HaN) cancer that requires accurate segmentation of target volumes and nearby healthy organs-at-risk (OARs) to optimize radiation dose distribution. However, computed tomography (CT) imaging has low image contrast for soft tissues, making accurate segmentation of soft tissue OARs challenging. Therefore, magnetic resonance (MR) imaging has been recommended to enhance the segmentation of soft tissue OARs in the HaN region. Based on our two empirical observations that deformable registration of CT and MR images of the same patient is inherently imperfect and that concatenating such images at the input layer of a deep learning network cannot optimally exploit the information provided by the MR modality, we propose a novel modality fusion module (MFM) that learns to spatially align MR-based feature maps before fusing them with CT-based feature maps. The proposed MFM can be easily implemented into any existing multimodal backbone network. Our implementation within the nnU-Net framework shows promising results on a dataset of CT and MR image pairs from the same patients. Furthermore, the evaluation on a clinically realistic scenario with the missing MR modality shows that MFM outperforms other state-of-the-art multimodal approaches.

KW - Computed tomography

KW - Head and neck

KW - Magnetic resonance

KW - Multimodal segmentation

KW - nnU-Net

KW - Organs-at-risk

UR - http://www.scopus.com/inward/record.url?scp=85174727548&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-43901-8_71

DO - 10.1007/978-3-031-43901-8_71

M3 - Article in proceedings

AN - SCOPUS:85174727548

SN - 9783031439001

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 745

EP - 755

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings

A2 - Greenspan, Hayit

A2 - Greenspan, Hayit

A2 - Madabhushi, Anant

A2 - Mousavi, Parvin

A2 - Salcudean, Septimiu

A2 - Duncan, James

A2 - Syeda-Mahmood, Tanveer

A2 - Taylor, Russell

PB - Springer

T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023

Y2 - 8 October 2023 through 12 October 2023

ER -