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Abstract #1439

Real-time large-scale anatomical landmark detection with limited medical images

Jun Zhang1, Mingxia Liu1, and Dinggang Shen1

1Radiology and BRIC, UNC at Chapel Hill, Chapel hill, NC, United States

Landmark detection based on deep neural networks has achieved state-of-the-art performance in natural image analysis. However, it is challenging to detect anatomical landmarks from medical images, due to limited data. Here, we propose a real-time large-scale landmark detection method with limited training data. We train our model with image patches and test it with the entire image, inspired by fully convolutional networks. Also, we develop a weighted loss function in our model to increase the correlations between image patches and their nearby landmarks. The experimental results of detecting 1741 landmarks from brain MR images demonstrate the effectiveness of our method.

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