Borzasi E

Capala ME

Cozzini C

Czabany R

Czipczer V

Cziria B

Czunyi E

Deak-Karancsi B

Egyud Z

Estkowsky L

Ferenczi L

Fronto A

Gaal S

Gyalai B

Hideghety K

Irmai BH

Karancsi Z

Kekesi A

Kelemen G

Keresnyei NG

Kolozsvari B

Koszo R

Maxwell R

McCallum H

Megyeri I

Mian M

Nagypal P

Paczona V

Pearson RA

Petit SF

Rusko L

Tan T

Tass BP

Vegvary Z

Wiesinger F

Wyatt J

English

Copyright \ua9 2023 Czipczer, Kolozsv\ue1ri, De\ue1k-Karancsi, Capala, Pearson, Borz\ue1si, Egy\ufcd, Ga\ue1l, Kelemen, K\uf3sz\uf3, Paczona, V\ue9gv\ue1ry, Karancsi, K\ue9kesi, Czunyi, Irmai, Keresnyei, Nagyp\ue1l, Czab\ue1ny, Gyalai, Tass, Cziria, Cozzini, Estkowsky, Ferenczi, Front\uf3, Maxwell, Megyeri, Mian, Tan, Wyatt, Wiesinger, Hidegh\ue9ty, McCallum, Petit and Rusk\uf3.Introduction: The excellent soft-tissue contrast of magnetic resonance imaging (MRI) is appealing for delineation of organs-at-risk (OARs) as it is required for radiation therapy planning (RTP). In the last decade there has been an increasing interest in using deep-learning (DL) techniques to shorten the labor-intensive manual work and increase reproducibility. This paper focuses on the automatic segmentation of 27 head-and-neck and 10 male pelvis OARs with deep-learning methods based on T2-weighted MR images. Method: The proposed method uses 2D U-Nets for localization and 3D U-Net for segmentation of the various structures. The models were trained using public and private datasets and evaluated on private datasets only. Results and discussion: Evaluation with ground-truth contours demonstrated that the proposed method can accurately segment the majority of OARs and indicated similar or superior performance to state-of-the-art models. Furthermore, the auto-contours were visually rated by clinicians using Likert score and on average, 81% of them was found clinically acceptable

Newcastle University E-Prints

Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning

https://eprints.ncl.ac.uk/file_store/production/294286/3F2A7777-6E73-4209-A597-EA87DD1ABA54.pdf

Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning

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