  Glioma is one of the most common types of brain tumors; it arises in the
glial cells in the human brain and in the spinal cord. In addition to having a
high mortality rate, glioma treatment is also very expensive. Hence, automatic
and accurate segmentation and measurement from the early stages are critical in
order to prolong the survival rates of the patients and to reduce the costs of
the treatment. In the present work, we propose a novel end-to-end cascaded
network for semantic segmentation that utilizes the hierarchical structure of
the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation
modules after each convolution and concatenation block. By utilizing
cross-validation, an average ensemble technique, and a simple post-processing
technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff
Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor
core, and enhancing tumor, respectively, on the online test set.
