Gradient-based generation of intermediate images for heterogeneous tumor segmentation within hybrid PET/MRI scans. (April 2020)

• Record Type: Journal Article
• Authors: Sbei, Arafet; ElBedoui, Khaoula; Barhoumi, Walid; Maktouf, Chokri
• Is Part Of: Computers in biology and medicine. Volume 119(2020)
• Publication Date: 2020-04
• Subjects: PET/MRI scans -- Heterogeneous tumors -- Intermediate images -- Tumor map -- Co-segmentation -- Gradient; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103669
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Segmentation of tumors from hybrid PET/MRI scans plays an essential role in accurate diagnosis and treatment planning. However, when treating tumors, several challenges, notably heterogeneity and the problem of leaking into surrounding tissues with similar high uptake, have to be considered. To address these issues, we propose an automated method for accurate delineation of tumors in hybrid PET/MRI scans. The method is mainly based on creating intermediate images. In fact, an automatic detection technique that determines a preliminary Interesting Uptake Region (IUR) is firstly performed. To overcome the leakage problem, a separation technique is adopted to generate the final IUR. Then, smart seeds are provided for the Graph Cut (GC) technique to obtain the tumor map. To create intermediate images that tend to reduce heterogeneity faced on the original images, the tumor map gradient is combined with the gradient image. Lastly, segmentation based on the G C s u m m a x technique is applied to the generated images. The proposed method has been validated on PET phantoms as well as on real-world PET/MRI scans of prostate, liver and pancreatic tumors. Experimental comparison revealed the superiority of the proposed method over state-of-the-art methods. This confirms the crucial role of automatically creating intermediate images in addressing the problem of wrongly estimating arc weights for heterogeneous targets. Highlights: Segmentation method for both homogeneous and heterogeneous tumors. Preliminary IUR includes necrosis and regions with cystic change within the tumor. Separation technique excludes regions with high uptake similar to tumor from the preliminary IUR. Intermediate images for PET and MRI are defined by combining tumor map gradient and gradient image. Validation on PET phantoms and real-world PET/MRI scans of prostate, liver and pancreatic tumors.