Details

Autor(en) / Beteiligte

• Cui, Xueting
• Liang, Thony Yan
• Aghili, Marymossadat
• Adeyosoye, Micheal
• Cid, Rosie E. Curiel
• Lowenstein, David
• Duara, Ranjan
• Adjouadi, Malek

Titel

UNet++ with Attention Mechanism for Hippocampus Segmentation

Ist Teil von

• 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022, p.1530-1534

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Analyzing the hippocampus in the brain through magnetic resonance imaging (MRI) plays a crucial role in diagnosing and making treatment decisions for several neurological diseases. Hippocampus atrophy is among the most informative early diagnostic biomarkers of Alzheimer's disease (AD), yet its automatic segmentation is extremely difficult given the anatomical structure of the brain and the lack of any contrast in between its different regions. The gold standard remains manual segmentation and the use of brain atlases. In this study, we use a well-known image segmentation model, UNet++, and introduce an attention mechanism called the Convolutional Block Attention Module (CBAM) to the UNet++ model. This integrated model improves the feature weights of our region of interest, and hence increases the accuracy in segmenting the hippocampus. Results show averages of 0.8715, 0.8107, 0.8872, and 0.9039 for the metrics of Dice, Jaccard, Precision, and Recall, respectively.