GAN-MRI enhanced multi-organ MRI segmentation: a deep learning perspective.
Authors
Affiliations (7)
Affiliations (7)
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Republic of Singapore. [email protected].
- Aikenist Technologies Pvt. Ltd, 10th Main Road, 22nd Cross Rd, 3rd Block East, Jayanagar, Bengaluru, Karnataka, 560011, India.
- Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, No.42, Avadi-Vel Tech Road, Vel Nagar, Avadi, Chennai, Tamil Nadu, 600062, India.
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, 11 Jln Tan Tock Seng, Singapore, 308433, Singapore.
- Department of Computer Science, Dr. Ambedkar Institute of Technology, Outer Ring Rd, Near Gnana Bharathi, 2nd Stage, Naagarabhaavi, Bengaluru, Karnataka, 560056, India.
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Republic of Singapore.
- Clinical Imaging Research Centre (CIRC), Centre for Translational Medicine (MD6), Yong Loo Lin School of Medicine, National University of Singapore, #B1-01, 14 Medical Drive, Singapore, 117599, Singapore.
Abstract
Clinical magnetic resonance imaging (MRI) is a high-resolution tool widely used for detailed anatomical imaging. However, prolonged scan times often lead to motion artefacts and patient discomfort. Fast acquisition techniques can reduce scan times but often produce noisy, low-contrast images, compromising segmentation accuracy essential for diagnosis and treatment planning. To address these limitations, we developed an end-to-end framework that incorporates BIDS-based data organiser and anonymizer, a GAN-based MR image enhancement model (GAN-MRI), AssemblyNet for brain region segmentation, and an attention-residual U-Net with Guided loss for abdominal and thigh segmentation. Thirty brain scans (5,400 slices) and 32 abdominal (1,920 slices) and 55 thigh scans (2,200 slices) acquired from multiple MRI scanners (GE, Siemens, Toshiba) underwent evaluation. Image quality improved significantly, with SNR and CNR for brain scans increasing from 28.44 to 42.92 (p < 0.001) and 11.88 to 18.03 (p < 0.001), respectively. Abdominal scans exhibited SNR increases from 35.30 to 50.24 (p < 0.001) and CNR from 10,290.93 to 93,767.22 (p < 0.001). Double-blind evaluations highlighted improved visualisations of anatomical structures and bias field correction. Segmentation performance improved substantially in the thigh (muscle: + 21%, IMAT: + 9%) and abdominal regions (SSAT: + 1%, DSAT: + 2%, VAT: + 12%), while brain segmentation metrics remained largely stable, reflecting the robustness of the baseline model. Proposed framework is designed to handle data from multiple anatomies with variations from different MRI scanners and centres by enhancing MRI scan and improving segmentation accuracy, diagnostic precision and treatment planning while reducing scan times and maintaining patient comfort.