Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation

Article


Rehman, Azka, Usman, Muhammad, Shahid, Abdullah, Latif, Siddique and Qadir, Junaid. 2023. "Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation." Sensors. 23 (4). https://doi.org/10.3390/s23042346
Article Title

Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation

ERA Journal ID34304
Article CategoryArticle
AuthorsRehman, Azka, Usman, Muhammad, Shahid, Abdullah, Latif, Siddique and Qadir, Junaid
Journal TitleSensors
Journal Citation23 (4)
Article Number2346
Number of Pages16
Year2023
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1424-8220
1424-8239
Digital Object Identifier (DOI)https://doi.org/10.3390/s23042346
Web Address (URL)https://www.mdpi.com/1424-8220/23/4/2346
AbstractBrain tumors are among the deadliest forms of cancer, characterized by abnormal proliferation of brain cells. While early identification of brain tumors can greatly aid in their therapy, the process of manual segmentation performed by expert doctors, which is often time-consuming, tedious, and prone to human error, can act as a bottleneck in the diagnostic process. This motivates the development of automated algorithms for brain tumor segmentation. However, accurately segmenting the enhanced and core tumor regions is complicated due to high levels of inter- and intra-tumor heterogeneity in terms of texture, morphology, and shape. This study proposes a fully automatic method called the selective deeply supervised multi-scale attention network (SDS-MSA-Net) for segmenting brain tumor regions using a multi-scale attention network with novel selective deep supervision (SDS) mechanisms for training. The method utilizes a 3D input composed of five consecutive slices, in addition to a 2D slice, to maintain sequential information. The proposed multi-scale architecture includes two encoding units to extract meaningful global and local features from the 3D and 2D inputs, respectively. These coarse features are then passed through attention units to filter out redundant information by assigning lower weights. The refined features are fed into a decoder block, which upscales the features at various levels while learning patterns relevant to all tumor regions. The SDS block is introduced to immediately upscale features from intermediate layers of the decoder, with the aim of producing segmentations of the whole, enhanced, and core tumor regions. The proposed framework was evaluated on the BraTS2020 dataset and showed improved performance in brain tumor region segmentation, particularly in the segmentation of the core and enhancing tumor regions, demonstrating the effectiveness of the proposed approach. Our code is publicly available.
Keywords3D segmentation; brain tumor segmentation; selective deep supervision
ANZSRC Field of Research 20204299. Other health sciences
Byline AffiliationsHealthHub, Republic of Korea
Seoul National University, Korea
University of Southern Queensland
Qatar University, Qatar
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