Ali Ataei, M.Sc.

Ali Ataei M.Sc. | Ph.D. student; studies early detection of bone metastasis using neural networks | Orthopaedic Research Laboratory Nijmegen, radboudumc, radboud university medical centre nijmegen Ali.AtaeiAapjeradboudumc.nl
T: +31 (0) 24 36 55288
About Me
Research Impression
Publications

About Me

In 2009 I completed my study Biomedical Engineering, field of Biomechanics, with the thesis on “A new diagnostic tool to noninvasively detect spinal deformities”. Afterwards, I started doing my Masters in the field of Biomechanics at Iran University of Science and Technology, and graduated in 2012. My Masters was focused on “Evaluation of the Milwaukee brace effectiveness on scoliotic deformation treatment”.

In January 2018 I came to the Netherlands and started as a Ph.D. student at the Orthopaedic Research Lab under the supervision of Nico Verdonschot and Esther Tanck.
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Research Impression

Bone is a common organ for metastasis. Prostate, breast and lung cancers are most probable to spread to the skeleton. Early detection of the pathological abnormalities in bone is critical for precise staging and optimal treatment. As some metastatic lesions come with complicated patterns to detect, specialists encounter difficulties in marking the exact location of them on CT-scan images. Hence, utilizing an automated segmentation mechanism using convolutional neural networks can aid in detecting the metastatic region. Therefore, this project aims to investigate the use of deep learning for recognizing the bone pathological abnormalities due to cancer.

Osteolytic and osteoblastic metastatic lesions in a proximal femur; Ali Ataei | Orthopaedic Research Laboratory Nijmegen, radboudumc, Radboud university medical centre
Figure 1: Transverse cross-section, showing a mix of osteolytic (abnormal bone resorption) and osteoblastic (abnormal bone formation) metastatic lesions in the right proximal femur.

In this Ph.D. project, three different types of bone metastasis (osteolytic, osteoblastic and mixed) in the proximal femur (fig.1) will be taken into account. A set of CT-scan images containing pathological bone will be used to train and test the neural network which will be customized to detect the metastatic region. In combination with a statistical analysis on geometry and bone densities in the proximal femur, different characteristics of detected metastatic lesions such as shape, size and the location could eventually lead to a strength prediction on pathological bone. Based on the strength prediction, specialists could opt for treatment strategies including radiotherapy, surgery, etc. in order to decrease the fracture risk.
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Publications

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