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The Artificial Intelligence (AI)-supported data analysis is widely adopted in various domains to achieve better result for a chosen task. In medical domain, the AI-supported image analysis is commonly adopted to automate the image examination task. This research aims to propose a Deep Learning (DL)-based segmentation tool to extract the Pituitary Gland (PG) from the sagittal-plane brain MRI slice. The various stages in the proposed scheme includes: (1) image and mask collection from the repository, (2) three-dimension (3D) image to 2D image conversion using ITK-Snap and resizing, (3) pre-processing the MRI slice using Kapur’s Entropy and Butterfly Algorithm (KE + BA)-based thresholding, (4) implementing the VGG-UNet and extracting the PG with better accuracy, and (5) computing the necessary image metrics by comparing segmented PG with mask. This work implements the segmentation operation on the unprocessed and pre-processed MRI slices and verifies the performance of the implemented scheme based on the achieved image metrics. The experimental outcome authenticates that the VGG-UNet helps to achieve better Jaccard (91.37 ± 0.14), Dice (96.83 ± 0.04), and Accuracy (97.08 ± 0.02) compared to the unprocessed brain MRI slices. This confirms that the proposed DL-tool works well for the chosen image database.

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Pituitary Gland Segmentation from Pre-processed Brain MRI Slice with VGG-UNet

  • 会议论文

  • 发表日期：

    2026年

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  • Seifedine Kadry,
  • Sahar Yassine,
  • Hong Lin,
  • Venkatesan Rajinikanth,
  • Ömer Melih Gül

摘要

The Artificial Intelligence (AI)-supported data analysis is widely adopted in various domains to achieve better result for a chosen task. In medical domain, the AI-supported image analysis is commonly adopted to automate the image examination task. This research aims to propose a Deep Learning (DL)-based segmentation tool to extract the Pituitary Gland (PG) from the sagittal-plane brain MRI slice. The various stages in the proposed scheme includes: (1) image and mask collection from the repository, (2) three-dimension (3D) image to 2D image conversion using ITK-Snap and resizing, (3) pre-processing the MRI slice using Kapur’s Entropy and Butterfly Algorithm (KE + BA)-based thresholding, (4) implementing the VGG-UNet and extracting the PG with better accuracy, and (5) computing the necessary image metrics by comparing segmented PG with mask. This work implements the segmentation operation on the unprocessed and pre-processed MRI slices and verifies the performance of the implemented scheme based on the achieved image metrics. The experimental outcome authenticates that the VGG-UNet helps to achieve better Jaccard (91.37 ± 0.14), Dice (96.83 ± 0.04), and Accuracy (97.08 ± 0.02) compared to the unprocessed brain MRI slices. This confirms that the proposed DL-tool works well for the chosen image database.