Puskar Pattanayak1, Evrim B Turkbey1, Ronald M Summers2. 1. Clinical Image Processing Service, National Institutes of Health Clinical Center, Bethesda, Maryland. 2. Clinical Image Processing Service, National Institutes of Health Clinical Center, Bethesda, Maryland; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr., Building 10, Rm. 1C224D MSC 1182, Bethesda, MD 20892-1182. Electronic address: rms@nih.gov.
Abstract
RATIONALE AND OBJECTIVES: This study aims to compare the speed and accuracy of three different software packages in segmenting the liver and the spleen. MATERIALS AND METHODS: The three software packages are Advantage Workstation Solutions (AWS), Claron Technology (Claron) Liver Segmentor, and Vitrea Core Fx (Vitrea). The dataset consisted of abdominal computed tomography scans of 30 patients obtained from the portal venous phase. All but two of the patients had a cancer diagnosis. The livers of 14 patients and the spleens of 24 patients were reported as normal; the remaining livers and spleens contained one or more abnormalities. The initial segmentation times and volumes were recorded in Claron and Vitrea as these created automatic segmentations. The total segmentation times and volumes following corrections were recorded. The livers and spleens were segmented separately by two radiologists who used all three packages. Accuracy was assessed by comparing volumes measured using fully manual segmentation on the AWS. RESULTS: Claron could not segment the spleen in four subjects for the first reader and in two subjects for the second reader. The final mean segmentation times for the liver for both readers were 6.5 and 5.5 minutes for AWS, 4.4 and 3.6 minutes for Claron, and 5.1 and 4.2 minutes for Vitrea. The final mean segmentation times for the spleen were 2.7 and 2.1 minutes for AWS, 2.1 and 1.4 minutes for Claron, and 1.8 and 1.2 minutes for Vitrea. No statistically significant difference was found between the organ volumes measured by the two readers when using Vitrea. The mean differences between the initial and final segmentation volumes ranged from -1.2% to 0.4% for the liver and from -4.0% to 9.8% for the spleen. The mean differences between the automated liver segmentation volumes and the AWS volumes were 2.5%-2.9% for Claron and 4.9%-6.6% for Vitrea. The mean differences between the automated splenic segmentation volumes and the AWS volumes were 5.0%-6.2% for Claron and 10.6%-12.0% for Vitrea. CONCLUSIONS: Both automated packages (Claron and Vitrea) measured liver and spleen volumes that were accurate and quick before manual correction. Volumes for the liver were more accurate than those for the spleen, perhaps due to the much smaller splenic volumes compared to those of the liver. For both liver and spleen, manual corrections were time consuming and for most subjects did not significantly change the volume measurement.
RATIONALE AND OBJECTIVES: This study aims to compare the speed and accuracy of three different software packages in segmenting the liver and the spleen. MATERIALS AND METHODS: The three software packages are Advantage Workstation Solutions (AWS), Claron Technology (Claron) Liver Segmentor, and Vitrea Core Fx (Vitrea). The dataset consisted of abdominal computed tomography scans of 30 patients obtained from the portal venous phase. All but two of the patients had a cancer diagnosis. The livers of 14 patients and the spleens of 24 patients were reported as normal; the remaining livers and spleens contained one or more abnormalities. The initial segmentation times and volumes were recorded in Claron and Vitrea as these created automatic segmentations. The total segmentation times and volumes following corrections were recorded. The livers and spleens were segmented separately by two radiologists who used all three packages. Accuracy was assessed by comparing volumes measured using fully manual segmentation on the AWS. RESULTS:Claron could not segment the spleen in four subjects for the first reader and in two subjects for the second reader. The final mean segmentation times for the liver for both readers were 6.5 and 5.5 minutes for AWS, 4.4 and 3.6 minutes for Claron, and 5.1 and 4.2 minutes for Vitrea. The final mean segmentation times for the spleen were 2.7 and 2.1 minutes for AWS, 2.1 and 1.4 minutes for Claron, and 1.8 and 1.2 minutes for Vitrea. No statistically significant difference was found between the organ volumes measured by the two readers when using Vitrea. The mean differences between the initial and final segmentation volumes ranged from -1.2% to 0.4% for the liver and from -4.0% to 9.8% for the spleen. The mean differences between the automated liver segmentation volumes and the AWS volumes were 2.5%-2.9% for Claron and 4.9%-6.6% for Vitrea. The mean differences between the automated splenic segmentation volumes and the AWS volumes were 5.0%-6.2% for Claron and 10.6%-12.0% for Vitrea. CONCLUSIONS: Both automated packages (Claron and Vitrea) measured liver and spleen volumes that were accurate and quick before manual correction. Volumes for the liver were more accurate than those for the spleen, perhaps due to the much smaller splenic volumes compared to those of the liver. For both liver and spleen, manual corrections were time consuming and for most subjects did not significantly change the volume measurement.
Authors: Gabriel E Humpire-Mamani; Joris Bukala; Ernst T Scholten; Mathias Prokop; Bram van Ginneken; Colin Jacobs Journal: Radiol Artif Intell Date: 2020-07-22
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