Title: Deep neural networks for medical image segmentation: geodesic distance transform

Authors: P. Jenifer Darling Rosita; W. Stalin Jacob; R. Kalpana; T. Cynthia Anbuselvi

Addresses: Electrical Engineering Department, New Era College, Gaborone, Botswana ' Engineering Department, Botho University, Gaborone, Botswana ' Department of Electronics and Communication Engineering, VelTech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai, Tamilnadu, India ' S.E.A. College of Engineering and Technology, Karnataka – 560049, Bangalore, India

Abstract: The segmentation of medical images aids in managing the dose of medication, as well as the dosage of exposure to radiation, thereby limiting the development of diseases like tumours and monitoring the progression of diseases like cancer. The process of segmentation involves the division of a picture into distinct regions that each includes fragments of pixels that have similar characteristics. The regions should have a strong link to the items or elements of interest depicted in the picture in order to be expressive and useful for image analysis and interpretation. A proposal is made for an interactive framework that takes a deep learning approach. P-Net is the first stage of the framework, and it is used to produce an initial automated segmentation. The second stage is where the framework is implemented. This interaction is included in the input of the R-Net.

Keywords: image segmentation; deep learning; brain tumour; datasets.

DOI: 10.1504/IJMEI.2025.145846

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.3, pp.246 - 254

Received: 15 May 2022
Accepted: 11 Aug 2022
Published online: 30 Apr 2025 *

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