Abstract
Accurate identification of brain tumor sub-regions boundaries in MRI plays a profoundly important role in clinical applications, such as surgical treatment planning, image-guided interventions, monitoring tumor growth, and the generation of radiotherapy maps. However, manual delineation practices has suffered from many problems such as requiring anatomical knowledge, taking considerable time for annotation, showing inaccuracy due to human error. To tackle these issues, automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) has been used in recent years. In this work, a ResNet-like Encoder-Decoder architecture is trained on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2021 training dataset. Experimental results demonstrate that this work shows a faily good performance in brain tumor segmentation.
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Nguyen-Truong, H., Pham, QD. (2022). Dice Focal Loss with ResNet-like Encoder-Decoder Architecture in 3D Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_9
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