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Abstract #3510

A deep neural network with convolutional LSTM for brain tumor segmentation in multi-contrast volumetric MRI

Namho Jeong1, Byungjai Kim1, Jongyeon Lee1, and Hyunwook Park1
1Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

A medical image segmentation method is a key step in contouring of designs for radiotherapy planning and has been widely studied. In this work, we propose a method using inter-slice contexts to distinguish small objects such as tumor tissues in 3D volumetric MR images by adding recurrent neural network layers to existing 2D convolutional neural networks. It is necessary to apply a convolutional long-short term memory (ConvLSTM) since 3D volumetric data can be considered as a sequence of 2D slices. We verified through the analysis that the correlation between neighboring segmentation maps and the overall segmentation performance was improved.

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