Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

Abstract

Background Pancreas segmentation is of great significance for pancreatic cancer radiotherapy positioning, pancreatic structure, and function evaluation. Purpose To investigate the feasibility of computer-aided pancreas segmentation based on optimized three-dimensional (3D) Dixon magnetic resonance imaging (MRI). Material and Methods Seventeen healthy volunteers (13 men, 4 women; mean age = 53.4 ± 13.2 years; age range = 28–76 years) underwent routine and optimized 3D gradient echo (GRE) Dixon MRI at 3.0 T. The computer-aided segmentation of the pancreas was executed by the Medical Imaging Interaction ToolKit (MITK) with the traditional segmentation algorithm pipeline (a threshold method and a morphological method) on the opposed-phase and water images of Dixon. The performances of our proposed computer segmentation method were evaluated by Dice coefficients and two-dimensional (2D)/3D visualization figures, which were compared for the opposed-phase and water images of routine and optimized Dixon sequences. Results The dice coefficients of the computer-aided pancreas segmentation were 0.633 ± 0.080 and 0.716 ± 0.033 for opposed-phase and water images of routine Dixon MRI, respectively, while they were 0.415 ± 0.143 and 0.779 ± 0.048 for the optimized Dixon MRI, respectively. The Dice index was significantly higher based on the water images of optimized Dixon than those in the other three groups (all P values < 0.001), including water images of routine Dixon MRI and both of the opposed-phase images of routine and optimized Dixon sequences. Conclusion Computer-aided pancreas segmentation based on Dixon MRI is feasible. The water images of optimized Dixon obtained the best similarity with a good stability.

Repeatability and Reproducibility of Pancreas Volume Measurements Using MRI

“…Presently, the accuracy of automated pancreas segmentation has lagged behind other abdominal organs 18 , likely due to the pancreas being a small, flexible abdominal organ with a high degree of variation among individuals in both shape and volume. A previous study found that Dice coefficients between computer models of pancreas segmentation and manual segmentation by radiologist were agreeable across the head, body, and tail of the pancreas 19 . Another study demonstrated reproducibility in a hybrid gradient, region growth and shape constraint segmentation method across multiple subjects 20 .…”

Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks

“…Our segmentation of organs like duodenum (0.80) and small intestine (0.87) is even much better than their CT-base segmentation (0.75 in duodenum and 0.80 in the small intestine). For the most studied single organ -pancreas, our DSC 0.88 is still on par with recent state-of-the-art deep learning-based segmentation works [35][36][37][38].…”

“…For the most studied single organ pancreas, our DSC of 0.88 is still on par with recent stateof-the-art deep learning-based segmentation works. [45][46][47][48] We believe that there is still room to improve in our network, particularly for organs like the small intestine and duodenum. First, to strike a good balance between computational complexity in 2D and richer geometry context in 3D networks, we will explore the hierarchical multiresolution 3D approach.…”