IRONDB · MR-based analysis, Indexing, and Retrieval of brain irON Deposition in Basal ganglia
6РП — Действия „Мария Кюри“
- Период
- 2006-10-01 → 2010-09-30
- Финансиране от ЕС
- 408 309 €
- Участници
- 2
- Схема
- TOK
Линиите свързват координатора с партньорите.
Накратко на български
Натрупването на желязо в базалните ганглии на мозъка се анализира чрез магнитен резонанс и специализирани алгоритми за търсене. Това помага за по-доброто разпознаване и визуализиране на тези отлагания в мозъчната тъкан.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Final Activity Report Summary - IRONDB (MR-based analysis, Indexing, and Retrieval of brain irON Deposition in Basal ganglia)
The IRONDB project has been executed as planned in both time and deliverables. We recruited three fellows (Experienced Researchers) in time aligned with our plans. For all positions, there were many applicants showing the appeal of the project and collaboration. For Fellow3, the eligibility requirements of being a non-Turkish person and having at least 2 years of industrial experience reduced our candidate pool considerably. In the end, for that position as well, we could hire a very strong candidate. In the recruitment stage, we received feedback from both partners and sought the approvals of both Prof. Aytul Ercil and Prof. Mujdat Cetin. During the execution, we decided to cancel the shorter term stays and informed this in the previous reports. The cancellation has not affected the overall progress because we took measures to compensate for this by making short trips as well as having weekly teleconferences. For the scientific part, we have fulfilled the deliverables for the seven main workpackages we had described in the project proposal. These tasks include: 1. Collection of the dataset and ground truth, 2. Normalisation of the MR data and detection of hypo-intense voxels. 3. Organ segmentation. 4. Shape-based search and retrieval. 5. Expert-knowledge for semantic search and retrieval. 6. Visualisation. 7. Validation, demonstration, and documentation. We used both proprietary and public datasets for the development and testing of our algorithms. We initiated new clinical contacts, such as Anatolian Health Center, Istanbul and Yeditepe University Hospital, Istanbul. We could formalise our collaboration with Yeditepe University Hospital by taking part in a Clinical Investigation Agreement between Philips Healthcare and the university. To further improve the collaboration, especially at the technical level, an ftp site has been set up to share data, software, reports, and the experimental results. The fellows learned a great deal from the interactions with clinical experts and also made publications with them. We considered how to deploy our system in a hospital setting. We conducted a clinical validation of our search and retrieval system and showed that our system can improve the diagnostic decision process of radiologists. Throughout the project, we emphasised the collaboration among fellows despite the geographical distance between the partners. With visits, teleconferences, data and knowledge sharing, and common project definitions, we created ways to enhance this. For example, in early 2009, we started a collaborative activity on the GUI design among the three fellows and Dr Ekin. Teleconferences were held, a source repository was established at Philips, and new GUI was designed. In the transfer of Knowledge phase, Dr. Devrim Unay shared his knowledge in search and retrieval schemes with his colleagues at Sabanci University. This resulted in a joint paper published in the ImageCLEF contest, where the joint team received the fourth place. In total, 12 of 19 conference papers (more than 60 percent) included more than one fellow in the author list quantitatively proving the collaborations in the project. We were active in making publications in leading journals and conferences. Four journal papers were direct result of the fellows' work during their recruitment period; several more resulted from the initiatives the fellows took beforehand and continued during their tenures. The number of peer-reviewed publications was 23 (four journal, 19 conference papers) in 60 man-months making one publication per less than three man-months of the project. We also received interest from Philips Intellectual Property Office that filed three patent applications for our work. Overall, the project has achieved her aims by increasing the depth of brain MR analysis knowledge in both partners and by providing a common framework for them to share their knowledge that had been built before.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
The aging in Europe necessitates more effective healthcare solutions especially in neurology, for it is one of the greatest risk factors for certain neurological diseases that impose significant financial and emotional costs to the society, est. 139 billion euros per year, not including the early stages of the disease, the emotional pain of the family, and the loss of experienced people from the workforce. Early detection is essential for alleviation or prevention, but it requires thorough understanding of the chemical changes in the brain. One of the most salient chemical changes regarding these diseases is a large amount of iron accumulation in the basal ganglia (BG) organ of the brain. Thus, it is often claimed that iron could be a potential biomarker for such diseases. This is very appealing because tissues with high iron concentration appear hypo-intense in the T2 contrast of MR images.To this effect, this project aims at developing:1) automatic brain MR image analysis methods to detect iron in the BG, and2) search and retrieval tools to be able to interpret brain MR images by the similarity of iron accumulation in a large population.These tasks involve multi-disciplinary research from the areas image processing, pattern recognition, databases, and clinical science. Compared with alternatives, MR-based brain iron detection and analysis is cost-effective, fast, and non-invasive. Moreover, we will develop search and retrieval tools to compare patients to correlate the iron-related features with neurological diseases and non-obvious pathologies.This does not exist today and is revolutionary. With Philips Research Video Processing Group, with its knowledge on image processing, and search and retrieval, and Sabanci University VBALAB, with pattern recognition, machine learning and image analysis knowledge, this project comprises partners with complementary capabilities essential for the success of this project.
Оригинален текст от CORDIS (на английски).
Участници
- PHILIPS ELECTRONICS NEDERLAND B.V. · EINDHOVENКоординаторНидерландия
- SABANCI UNIVERSITESI · TUZLAТурция
Връзки
Данни: CORDIS, © Европейски съюз
