Computer-assisted medical image analysis for proton and radiotherapy treatment planning (J2-1732)

General information

Title
Computer-assisted medical image analysis for proton and radiotherapy treatment planning
Period
Jul 1, 2019 -- Jun 30, 2022
Range
1.87 FTE
Activity
2.06 - Engineering Sciences and Technologies / Systems and Cybernetics / Biomedical Tehnics

Abstract

Cancer is, in Slovenia as well as worldwide, a great public health problem, and among the key challenges of modern society in the contexts of health management and quality of life. In addition to surgery and chemotherapy, radiation and lately proton therapy are important and widespread cancer treatment options. In order to generate optimal radiation dose distributions, it is important to have a precise knowledge of the location and shape of the tumors as well as of nearby healthy organs at risk (OARs). For this purpose, three-dimensional (3D) images of the patient are acquired with the computed tomography (CT) and/or magnetic resonance (MR) imaging technique, and accurate and precise delineation of target volumes from such images represents the basis for the inverse planning of optimal radiation dose distribution. Our research hypothesis is that the delineation of target volumes in the head and neck area can be improved in the CT as well as MR image modality, and by exploiting the fused information from both imaging modalities, so as to reduce the subjectivity and time spent for manual delineation. Within the proposed research project we will therefore aim to (1) setup an annotated database of CT and MR images of the head and neck area with OARs precisely delineated by multiple observers on multiple occasions, (2) evaluate the generated image database from the perspective of intra-observer and inter-observer variability to generate reference delineation masks, (3) design and develop computer-assisted techniques based on state-of-the art technologies for automated segmentation of target volumes from 3D CT and/or MR images that are used for radiotherapy planning for the purpose of improving the accuracy and reliability of the resulting segmentation while reducing the time and effort required by the observer to verify or eventually modify the results, (4) design and setup a platform for an objective evaluation and comparison of the segmentation results by means of organizing a computational challenge, and (5) explore the possibilities of applying the developed methodology to related problems in the field of radiotherapy planning. It is expected that the results of the proposed research project will represent a break-through in the corresponding research field, and stimulate new research guidelines.

Phases of the project and their realization

2019
Work Package 1 - Image database / Activity 1.1 - Image collection
Completed
Work Package 1 - Image database / Activity 1.2 - Observer assignment
Completed
Work Package 1 - Image database / Activity 1.3 - Manual delineation
Completed
Work Package 2 - Database evaluation / Activity 2.1 - Qualitative evaluation
Completed
2020
Work Package 2 - Database evaluation / Activity 2.1 - Qualitative evaluation
Completed
Work Package 2 - Database evaluation / Activity 2.2 - Quantitative evaluation
Completed
Work Package 2 - Database evaluation / Activity 2.3 - Reference data
Completed
Work Package 2 - Database evaluation / Activity 2.4 - Dissemination of results
Completed
Work Package 3 - Image segmentation / Activity 3.1 - Literature review
Completed
Work Package 3 - Image segmentation / Activity 3.2 - Segmentation method
Completed
Work Package 3 - Image segmentation / Activity 3.3 - Landmark detection
Completed
Work Package 3 - Image segmentation / Activity 3.4 - Shape modeling
Completed
Work Package 3 - Image segmentation / Activity 3.5 - Method augmentation
Completed
2021
Work Package 3 - Image segmentation / Activity 3.2 - Segmentation method
Completed
Work Package 3 - Image segmentation / Activity 3.5 - Method augmentation
Completed
Work Package 3 - Image segmentation / Activity 3.6 - Dissemination of results
Completed
Work Package 4 - Computational challenge / Activity 4.1 - Challenge proposal
Completed
Work Package 4 - Computational challenge / Activity 4.2 - Challenge setup
Completed
Work Package 4 - Computational challenge / Activity 4.3 - Result evaluation
Completed
Work Package 4 - Computational challenge / Activity 4.4 - Result presentation
Completed
Work Package 4 - Computational challenge / Activity 4.5 - Dissemination of results
Completed
Work Package 5 - Prospect research / Activity 5.1 - Exploration of possibilities
Completed
2022
Work Package 5 - Prospect research / Activity 5.1 - Exploration of possibilities
Completed
Work Package 5 - Prospect research / Activity 5.2 - Framework application
Completed
Work Package 5 - Prospect research / Activity 5.3 - Dissemination of results
Completed

Bibliographics records

1.
Gašper Podobnik, Bulat Ibragimov, Elias Tappeiner, Chanwoong Lee, Jin Sung Kim, Zacharia Mesbah, Romain Modzelweski, Yihao Ma, Fan Yang, Mikołaj Rudecki, Marek Wodziński, Primož Peterlin, Primož Strojan, Tomaž Vrtovec: HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge. Radiotherapy and Oncology, 198:110410, 2024 [COBISS-ID:202738179 ] [doi:10.1016/j.radonc.2024.110410 ]
2.
Gašper Podobnik, Bulat Ibragimov, Primož Peterlin, Primož Strojan, Tomaž Vrtovec: vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images. Medical Physics, 51(3):2175-2186, 2024 [COBISS-ID:181670147 ] [doi:10.1002/mp.16924 ]
3.
Gašper Podobnik, Bulat Ibragimov, Primož Peterlin, Primož Strojan, Tomaž Vrtovec: Multimodal CT and MR segmentation of head and neck organs-at-risk. 26th International Conference on Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, Oct 8-12, Vancouver, Canada, Lecture Notes in Computer Science 14223:745-755, 2023 [COBISS-ID:171494147 ] [doi:10.1007/978-3-031-43901-8_71 ]
4.
Gašper Podobnik, Primož Strojan, Primož Peterlin, Bulat Ibragimov, Tomaž Vrtovec: HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge. Grand Challenge, 2023-2024 [COBISS-ID:149951747 ] [https://han-seg2023.grand-challenge.org ]
5.
Gašper Podobnik, Primož Strojan, Primož Peterlin, Bulat Ibragimov, Tomaž Vrtovec: HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset. Zenodo, 2023 [COBISS-ID:137181699 ] [doi:10.5281/zenodo.7442914 ]
6.
Gašper Podobnik, Primož Strojan, Primož Peterlin, Bulat Ibragimov, Tomaž Vrtovec: HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset. Medical Physics, 50(3):1917-1927, 2023 [COBISS-ID:136767491 ] [doi:10.1002/mp.16197 ]
7.
Gašper Podobnik, Bulat Ibragimov, Primož Strojan, Primož Peterlin, Tomaž Vrtovec: Segmentation of organs-at-risk from CT and MR images of the head and neck: baseline results. 19th IEEE Symposium on Biomedical Imaging - ISBI 2022, Mar 28-31, Kolkata, India, 2022 [COBISS-ID:106601987 ] [doi:10.1109/ISBI52829.2022.9761433 ]
8.
Gašper Podobnik, Primož Strojan, Primož Peterlin, Bulat Ibragimov, Tomaž Vrtovec: Parotid gland segmentation with nnU-Net: deployment scenario and inter-observer variability analysis. SPIE Medical Imaging 2022: Image Processing, Feb 20-24, San Diego, CA, USA, 12032:120321N, 2022 [COBISS-ID:106571523 ] [doi:10.1117/12.2609406 ]
9.
Tomaž Vrtovec, Domen Močnik, Primož Strojan, Franjo Pernuš, Bulat Ibragimov: Auto‐segmentation of organs at risk for head and neck radiotherapy planning: from atlas‐based to deep learning methods. Medical Physics, 47(9):e929-e950, 2020 [COBISS-ID:18653955 ] [doi:10.1002/mp.14320 ]
10.
Domen Močnik, Bulat Ibragimov, Lei Xing, Primož Strojan, Boštjan Likar, Franjo Pernuš, Tomaž Vrtovec: Segmentation of parotid glands from registered CT and MR images. Physica Medica, 52:33-41, 2018 [COBISS-ID:12068692 ] [doi:10.1016/j.ejmp.2018.06.012 ]