Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation
Purpose: With the advent of magnetic resonance imaging (MRI) guided radiation therapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods. Methods and materials: T2-w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-07-01
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| Series: | Advances in Radiation Oncology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2452109416300100 |
| _version_ | 1818929634456109056 |
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| author | Shuiping Gou, PhD Percy Lee, MD Peng Hu, PhD Jean-Claude Rwigema, MD Ke Sheng, PhD |
| author_facet | Shuiping Gou, PhD Percy Lee, MD Peng Hu, PhD Jean-Claude Rwigema, MD Ke Sheng, PhD |
| author_sort | Shuiping Gou, PhD |
| collection | DOAJ |
| description | Purpose: With the advent of magnetic resonance imaging (MRI) guided radiation therapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.
Methods and materials: T2-weighted half-Fourier acquisition single-shot turbo spin-echo and T1 weighted volumetric interpolated breath-hold examination images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging, distance regularized level set, and graph cuts, and the segmentation results were compared with manual contours using Dice's index, Hausdorff distance, and shift of the center of the organ (SHIFT).
Results: All volumetric interpolated breath-hold examination images were successfully segmented by at least 1 of the autosegmentation method with Dice's index >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of half-Fourier acquisition single-shot turbo spin-echo images was significantly greater. DL is statistically superior to the other methods in Dice’s overlapping index. For the Hausdorff distance and SHIFT measurement, distance regularized level set and DL performed slightly superior to the graph cuts method, and substantially superior to mean-shift merging. DL required least human supervision and was faster to compute.
Conclusions: Our study demonstrated potential feasibility of automated segmentation of the pancreas on MRI scans with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization. |
| first_indexed | 2024-12-20T03:47:55Z |
| format | Article |
| id | doaj.art-d919e7642c894d09a7be157e07172518 |
| institution | Directory Open Access Journal |
| issn | 2452-1094 |
| language | English |
| last_indexed | 2024-12-20T03:47:55Z |
| publishDate | 2016-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Radiation Oncology |
| spelling | doaj.art-d919e7642c894d09a7be157e071725182022-12-21T19:54:33ZengElsevierAdvances in Radiation Oncology2452-10942016-07-011318219310.1016/j.adro.2016.05.002Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentationShuiping Gou, PhD0Percy Lee, MD1Peng Hu, PhD2Jean-Claude Rwigema, MD3Ke Sheng, PhD4Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shanxi, ChinaDepartment of Radiation Oncology, University of California, Los Angeles, CaliforniaDepartment of Radiological Science, University of California, Los Angeles, CaliforniaDepartment of Radiation Oncology, University of California, Los Angeles, CaliforniaDepartment of Radiation Oncology, University of California, Los Angeles, CaliforniaPurpose: With the advent of magnetic resonance imaging (MRI) guided radiation therapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods. Methods and materials: T2-weighted half-Fourier acquisition single-shot turbo spin-echo and T1 weighted volumetric interpolated breath-hold examination images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging, distance regularized level set, and graph cuts, and the segmentation results were compared with manual contours using Dice's index, Hausdorff distance, and shift of the center of the organ (SHIFT). Results: All volumetric interpolated breath-hold examination images were successfully segmented by at least 1 of the autosegmentation method with Dice's index >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of half-Fourier acquisition single-shot turbo spin-echo images was significantly greater. DL is statistically superior to the other methods in Dice’s overlapping index. For the Hausdorff distance and SHIFT measurement, distance regularized level set and DL performed slightly superior to the graph cuts method, and substantially superior to mean-shift merging. DL required least human supervision and was faster to compute. Conclusions: Our study demonstrated potential feasibility of automated segmentation of the pancreas on MRI scans with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization.http://www.sciencedirect.com/science/article/pii/S2452109416300100 |
| spellingShingle | Shuiping Gou, PhD Percy Lee, MD Peng Hu, PhD Jean-Claude Rwigema, MD Ke Sheng, PhD Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation Advances in Radiation Oncology |
| title | Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation |
| title_full | Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation |
| title_fullStr | Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation |
| title_full_unstemmed | Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation |
| title_short | Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation |
| title_sort | feasibility of automated 3 dimensional magnetic resonance imaging pancreas segmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2452109416300100 |
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