Work

BrainPixGAN: Generating intraoperative MRI images with mask-based generative networks

Year: 2024

Type: article

Abstract: In recent years, efforts to enhance precision in brain tumor surgeries have focused on using artificial intelligence (AI) with medical imaging . This involves integrating AI with medical imaging. This study aimed to generate a tumor-free MRI by using Generative Adversarial Networks (GANs) to establish a relationship between preoperative magnetic resonance imaging (MRI) and resection cavity segmentation masks obtained from intraoperative ultrasound (IOUS) data. For cavity segmentation, U-Net and U-Net with transfer learning were used, with the U-Net + EfficientNetB7 model achieving a high dice score of 97.82. The resection cavity mask was applied to preoperative MRI images using Pix2Pix, SPADE GAN, and BrainPixGAN. BrainPixGAN, incorporating transfer learning , outperformed the others, achieving SSIM 0.87, PSNR 35.89, and LPIPS 0.0037. This innovative approach represents a pioneering effort in generating GAN models for intraoperative MRI (iMRI) images using IOUS data, despite the challenges in setup and cost associated with iMRI imaging.

Source: Engineering Science and Technology an International Journal

DOI: 10.1016/j.jestch.2024.101827

Authors: Ayşe Gül Eker, Meltem Kurt Pehlivanoğlu, Nevcihan Duru, Tolga Turan Dündar

Institutions: Kocaeli Üniversitesi, Kocaeli Sağlık ve Teknoloji Üniversitesi, Bezmiâlem Vakıf Üniversitesi

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,270