Name
Title | ||
|---|---|---|
Advancing Pancreas Segmentation in Multi-protocol MRI Volumes Using Hausdorff-Sine Loss Function. |
Abstract | ||
|---|---|---|
Computing pancreatic morphology in 3D radiological scans could provide significant insight about a medical condition. However, segmenting the pancreas in magnetic resonance imaging (MRI) remains challenging due to high inter-patient variability. Also, the resolution and speed of MRI scanning present artefacts that blur the pancreas bound-aries between overlapping anatomical structures. This paper proposes a dual-stage automatic segmentation method: (1) a deep neural network is trained to address the problem of vague organ boundaries in high class-imbalanced data. This network integrates a novel loss function to rigorously optimise boundary delineation using the modified Hausdorff metric and a sinusoidal component; (2) Given a test MRI volume, the output of the trained network predicts a sequence of targeted 2D pancreas classes that are reconstructed as a volumetric binary mask. An energy-minimisation approach fuses a learned digital contrast model to suppress the intensities of non-pancreas classes, which, combined with the binary volume performs a refined segmentation in 3D while revealing dense boundary detail. Experiments are performed on two diverse MRI datasets containing 180 and 120 scans, in which the proposed approach achieves a mean Dice score of 84.1 +/- 4.6% and 85.7 +/- 2.3%, respectively. This approach is statistically stable and outperforms state-of-the-art methods on MRI. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32692-0_4 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Automatic pancreas segmentation,Energy-minimisation,MRI,Hausdorff loss function | Conference | 11861 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hykoush Asaturyan | 1 | 0 | 1.35 |
| E. Louise Thomas | 2 | 1 | 1.04 |
| Julie Fitzpatrick | 3 | 0 | 0.34 |
| Jimmy D Bell | 4 | 4 | 2.34 |
| Barbara Villarini | 5 | 17 | 4.13 |