TY  - GEN
AV  - public
Y1  - 2018///
EP  - 190
TI  - Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
T3  - Lecture Notes in Computer Science
KW  - Brain tumor
KW  -  Convolutional neural network
KW  -  Segmentation
CY  - Cham, Switzerland
PB  - Springer
N2  - A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core. The cascade is designed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step. The enhancing tumor core is then segmented based on the bounding box of the tumor core segmentation result. Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives. Residual connections and multi-scale predictions are employed in these networks to boost the segmentation performance. Experiments with BraTS 2017 validation set show that the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for enhancing tumor core, whole tumor and tumor core, respectively. The corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and 0.7748, respectively.
ID  - discovery1572791
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SP  - 178
A1  - Wang, G
A1  - Li, W
A1  - Ourselin, S
A1  - Vercauteren, T
UR  - https://doi.org/10.1007/978-3-319-75238-9_16
SN  - 1611-3349
ER  -