Abstract #3564

Landmark detection of fetal pose in volumetric MRI via deep reinforcement learning

Molin Zhang¹, Junshen Xu¹, Esra Turk², Borjan Gagoski^2,3, P. Ellen Grant^2,3, Polina Golland^1,4, and Elfar Adalsteinsson^1,5

¹Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ²Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ³Harvard Medical School, Boston, MA, United States, ⁴Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, United States, ⁵Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States

Fetal pose estimation could play an important role in fetal motion tracking or automatic fetal slice prescription by real-time adjustments of the prescribed imaging orientation based on fetal pose and motion patterns. In this abstract, we used a multiple image scale deep reinforcement learning method (DQN) to train an agent finding the target landmark of fetal pose by optimizing searching policy based on landmark features and its surroundings. Under an error tolerance of 15-mm, the detection accuracy reaches 58%.

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