PhD topic proposal: MAP-i
Scientific Area:
Computer Vision, Machine/Deep Learning, Cross-model knowledge transfer, Bone Marrow Edema
Description:
A bone marrow edema (BME) occurs when fluid builds up in the bone marrow. BME is typically a response to a fracture or conditions such as osteoarthritis, and it is characterized by excessive water signals in the marrow space on MRI and is not normally visible on plain X-ray or computed tomography images. BME can be identified by a health professional in areas like the hip, the ankle, the foot, the shoulder, but most often in the knee using an MRI scan, which is detected by a dark zone in T1 or a white zone in T2 images. The identification and assessment of BME are challenging and time‐consuming due to the need to assess multiple imaging planes. The automatic methods will allow early detection and decrease the subjectivity of the assessment.
X-Ray and MRI are two commonly-used imaging modalities in the diagnosis of bone edema. While X-Ray is the first line of medical examination, MRI is a more costly and time-consuming study, typically used for severe cases or with high suspicion of edema. Cross-modal transfer learning between X-Ray and MRI can therefore be advantageous to improve the classification performance in the absence of sufficient training data from MRI. On the other hand, bone edema is only visible in X-Ray for severe cases, and the MRI can be used in cross-modal deep learning to help the learning model to make the detection in the X-Ray from the knowledge learning from MRI classification.
The models to be developed will allow the use of MRI and X-ray for knee BME evaluation, with different strategies depending on the type of medical imaging. In particular, the objectives of this proposal are:
1) Detect BME in X-ray scans in order to help early-detection;
2) Quantify BME in MRI scans for late-stage evaluation;
3) Development of strategies to overcome the lack of massive label data, such as cross-domain and modality-bridge transfer learning;
4) Use cross-modal deep learning to improve the classification performance. Allowing to use knowledge learning on the MRI to the X-Ray, since in X-Ray, there is less visible information of bone edema and as consequence, the BME detection is more difficult.
The collaboration with Hospital São João in previous projects allowed the collection of a specific dataset and the correspondent annotation of the data that can be used in the current proposal.
References:
https://pubmed.ncbi.nlm.nih.gov/23825184/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2077908/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8302286 https://ieeexplore.ieee.org/document/7950480
Supervisor:
Tania Pereira, INESC TEC
Co-Supervisor:
Hélder Filipe Pinto de Oliveira, University of Porto, INESC TEC
Host Institution:
INESC TEC
External reviewer:
João Rodrigues, University of Algarve
