@inproceedings{213e57e734cd44318de2772d29e1e8d7,
title = "3D Patch Spatially Localized Network Tiles Enables for 3D Brain Segmentation",
abstract = "Brain cancer is so deadly that diagnosis accuracy will be required before brain surgery. Segmentation technology is important for medical imaging. CNN model is capable of searching for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. We propose a 3D U-Net network that was improved and trained with the T1ce weighted MRI scan to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be use in different ways. This study, utilizes the GPU in training and to reduce the computing time of the 3D PSLNT method which is able to patch multichannel images and all the data patches created by the training images are known as label maps. Each patch is set for a test image and similar patches are taken from the dataset. The matching labels for each of these patches are then combined to produce an initial segmentation map for the test cases.",
keywords = "3D u-net, PSLNT, dataset-brats 2021, network tiles, whole brain tumor segmentation",
author = "Pandapotan Siagian and Riyanarto Sarno and Chastine Fatichah and {Arrasyid Supriyanto}, {Muhammad Ibadurrahman} and Aziz Fajar",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 29th International Conference on Telecommunications, ICT 2023 ; Conference date: 08-11-2023 Through 09-11-2023",
year = "2023",
doi = "10.1109/ICT60153.2023.10374062",
language = "English",
series = "Proceedings - ICT 2023 - 29th International Conference on Telecommunications: Next-Generation Telecommunications for Digital Inclusion and Universal Access",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - ICT 2023 - 29th International Conference on Telecommunications",
address = "United States",
}
