  Automated segmentation proves to be a valuable tool in precisely detecting
tumors within medical images. The accurate identification and segmentation of
tumor types hold paramount importance in diagnosing, monitoring, and treating
highly fatal brain tumors. The BraTS challenge serves as a platform for
researchers to tackle this issue by participating in open challenges focused on
tumor segmentation. This study outlines our methodology for segmenting tumors
in the context of two distinct tasks from the BraTS 2023 challenge: Adult
Glioma and Pediatric Tumors. Our approach leverages two encoder-decoder-based
CNN models, namely SegResNet and MedNeXt, for segmenting three distinct
subregions of tumors. We further introduce a set of robust postprocessing to
improve the segmentation, especially for the newly introduced BraTS 2023
metrics. The specifics of our approach and comprehensive performance analyses
are expounded upon in this work. Our proposed approach achieves third place in
the BraTS 2023 Adult Glioma Segmentation Challenges with an average of 0.8313
and 36.38 Dice and HD95 scores on the test set, respectively.
