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

Seifedine Kadry; Sahar Yassine; Hong Lin; Venkatesan Rajinikanth; Ömer Melih Gül

doi:10.1007/978-3-031-92143-8_4

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

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

^*Corresponding author for this work

Informatics Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	8th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2024
Editors	Behçet Ugur Töreyin, Hatice Köse, Nizamettin Aydin, Ömer Melih Gül, Seifedine Nimer Kadry
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	49-61
Number of pages	13
ISBN (Print)	9783031921421
DOIs	https://doi.org/10.1007/978-3-031-92143-8_4
Publication status	Published - 2026
Event	8th EAI International Conference on Robotics and Networks, EAI ROSENET 2024 - Crete, Greece Duration: 3 Sept 2024 → 5 Sept 2024

Publication series

Name	EAI/Springer Innovations in Communication and Computing
ISSN (Print)	2522-8595
ISSN (Electronic)	2522-8609

Conference

Conference	8th EAI International Conference on Robotics and Networks, EAI ROSENET 2024
Country/Territory	Greece
City	Crete
Period	3/09/24 → 5/09/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Brain MRI
Evaluation
Healthcare
Kapur’s thresholding
Segmentation

Access to Document

10.1007/978-3-031-92143-8_4

Cite this

Kadry, S., Yassine, S., Lin, H., Rajinikanth, V., & Gül, Ö. M. (2026). Pituitary Gland Segmentation from Pre-processed Brain MRI Slice with VGG-UNet. In B. U. Töreyin, H. Köse, N. Aydin, Ö. Melih Gül, & S. Nimer Kadry (Eds.), 8th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2024 (pp. 49-61). (EAI/Springer Innovations in Communication and Computing). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-92143-8_4

Kadry, Seifedine ; Yassine, Sahar ; Lin, Hong et al. / Pituitary Gland Segmentation from Pre-processed Brain MRI Slice with VGG-UNet. 8th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2024. editor / Behçet Ugur Töreyin ; Hatice Köse ; Nizamettin Aydin ; Ömer Melih Gül ; Seifedine Nimer Kadry. Springer Science and Business Media Deutschland GmbH, 2026. pp. 49-61 (EAI/Springer Innovations in Communication and Computing).

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title = "Pituitary Gland Segmentation from Pre-processed Brain MRI Slice with VGG-UNet",

abstract = "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{\textquoteright}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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author = "Seifedine Kadry and Sahar Yassine and Hong Lin and Venkatesan Rajinikanth and G{\"u}l, \{{\"O}mer Melih\}",

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Kadry, S, Yassine, S, Lin, H, Rajinikanth, V & Gül, ÖM 2026, Pituitary Gland Segmentation from Pre-processed Brain MRI Slice with VGG-UNet. in BU Töreyin, H Köse, N Aydin, Ö Melih Gül & S Nimer Kadry (eds), 8th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2024. EAI/Springer Innovations in Communication and Computing, Springer Science and Business Media Deutschland GmbH, pp. 49-61, 8th EAI International Conference on Robotics and Networks, EAI ROSENET 2024, Crete, Greece, 3/09/24. https://doi.org/10.1007/978-3-031-92143-8_4

Pituitary Gland Segmentation from Pre-processed Brain MRI Slice with VGG-UNet. / Kadry, Seifedine; Yassine, Sahar; Lin, Hong et al.
8th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2024. ed. / Behçet Ugur Töreyin; Hatice Köse; Nizamettin Aydin; Ömer Melih Gül; Seifedine Nimer Kadry. Springer Science and Business Media Deutschland GmbH, 2026. p. 49-61 (EAI/Springer Innovations in Communication and Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Pituitary Gland Segmentation from Pre-processed Brain MRI Slice with VGG-UNet

AU - Kadry, Seifedine

AU - Yassine, Sahar

AU - Lin, Hong

AU - Rajinikanth, Venkatesan

AU - Gül, Ömer Melih

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - 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.

AB - 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.

KW - Brain MRI

KW - Evaluation

KW - Healthcare

KW - Kapur’s thresholding

KW - Segmentation

UR - https://www.scopus.com/pages/publications/105025001962

U2 - 10.1007/978-3-031-92143-8_4

DO - 10.1007/978-3-031-92143-8_4

M3 - Conference contribution

AN - SCOPUS:105025001962

SN - 9783031921421

T3 - EAI/Springer Innovations in Communication and Computing

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EP - 61

BT - 8th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2024

A2 - Töreyin, Behçet Ugur

A2 - Köse, Hatice

A2 - Aydin, Nizamettin

A2 - Melih Gül, Ömer

A2 - Nimer Kadry, Seifedine

PB - Springer Science and Business Media Deutschland GmbH

T2 - 8th EAI International Conference on Robotics and Networks, EAI ROSENET 2024

Y2 - 3 September 2024 through 5 September 2024

ER -

Kadry S, Yassine S, Lin H, Rajinikanth V, Gül ÖM. Pituitary Gland Segmentation from Pre-processed Brain MRI Slice with VGG-UNet. In Töreyin BU, Köse H, Aydin N, Melih Gül Ö, Nimer Kadry S, editors, 8th EAI International Conference on Robotic Sensor Networks - EAI ROSENET 2024. Springer Science and Business Media Deutschland GmbH. 2026. p. 49-61. (EAI/Springer Innovations in Communication and Computing). doi: 10.1007/978-3-031-92143-8_4

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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