Abhijith Chunduru

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We present our entry for the Longitudinal Multiple Sclerosis Challenge 2015 using 3D convolutional neural networks (CNN). We model a voxel-wise classifier using multi-channel 3D patches of MRI volumes as input. For each ground truth, a CNN is trained and the final segmentation is obtained by combining the probability outputs of these CNNs. Efficient(More)
In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions(More)
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