Mark L. Epstein

Quantitative Radiology: Automated CT Liver Volumetry Compared With Interactive Volumetry and Manual Volumetry

OBJECTIVE The purpose of this study was to evaluate automated CT volumetry in the assessment of living-donor livers for transplant and to compare this technique with software-aided interactive volumetry and manual volumetry. MATERIALS AND METHODS Hepatic CT scans of 18 consecutively registered prospective liver donors were obtained under a liver transplant protocol. Automated liver volumetry was developed on the basis of 3D active-contour segmentation. To establish reference standard liver volumes, a radiologist manually traced the contour of the liver on each CT slice. We compared the results obtained with automated and interactive volumetry with those obtained with the reference standard for this study, manual volumetry. RESULTS The average interactive liver volume was 1553 ± 343 cm(3), and the average automated liver volume was 1520 ± 378 cm(3). The average manual volume was 1486 ± 343 cm(3). Both interactive and automated volumetric results had excellent agreement with manual volumetric results (intraclass correlation coefficients, 0.96 and 0.94). The average user time for automated volumetry was 0.57 ± 0.06 min/case, whereas those for interactive and manual volumetry were 27.3 ± 4.6 and 39.4 ± 5.5 min/case, the difference being statistically significant (p < 0.05). CONCLUSION Both interactive and automated volumetry are accurate for measuring liver volume with CT, but automated volumetry is substantially more efficient.

Computed tomography liver volumetry using 3-dimensional image data in living donor liver transplantation: Effects of the slice thickness on the volume calculation†

The purpose of this study was to evaluate the relationship between the slice thickness and the calculated volume in computed tomography (CT) liver volumetry through the comparison of the results from images [including 3‐dimensional (3D) images] with various slice thicknesses. Twenty potential adult liver donors (12 men and 8 women) with a mean age of 39 years (range = 24‐64 years) underwent CT with a 64‐section multidetector row CT scanner after the intravenous injection of a contrast material. Four image sets with slice thicknesses of 0.625, 2.5, 5, and 10 mm were used. First, a program developed in our laboratory for automated liver extraction was applied to the CT images, and the liver boundaries were determined automatically. Then, an abdominal radiologist reviewed all images onto which automatically extracted boundaries had been superimposed and then edited the boundaries on each slice to enhance the accuracy. The liver volumes were determined via the counting of the voxels within the liver boundaries. The mean whole liver volumes estimated with CT were 1322.5 cm3 from 0.625‐mm images, 1313.3 cm3 from 2.5‐mm images, 1310.3 cm3 from 5‐mm images, and 1268.2 cm3 from 10‐mm images. The volumes calculated from 3D (0.625‐mm) images were significantly larger than the volumes calculated from thicker images (P < 0.001). The partial liver volumes of right lobes, left lobes, and lateral segments were evaluated in a similar manner. The estimated maximum difference in the calculated volumes of lateral segments was −10.9 cm3 (−4.63%) between 0.625‐ and 5‐mm images. In conclusion, liver volumes calculated from 2.5‐mm‐thick or thicker images are significantly smaller than liver volumes calculated from 3D images. If a maximum error of 5% in the calculated graft volume will not have a significant clinical impact, 5‐mm‐thick images are acceptable for CT volumetry. If the impact is significant, 3D images could be essential. Liver Transpl, 2011.

Hessian Matrix-Based Shape Extraction and Volume Growing for 3D Polyp Segmentation in CT Colonography

The size of a lesion is one of the most important features for medical decision-making. The lesion size is usually measured by a clinician, and existing practice relies on size representation using the single longest dimension. There are, however, established variations among clinicians in manual measurement of lesion size, and a recent study implies volume measurement to be more clinically informative than the linear representation (Pickhardt et al., 2005). Volume metrics captures the real growth, and changes in size are amplified compared to those in one dimension. As a further hurdle, modern medical imaging systems produce images on which a polyp appears in a series of 2D slices -measuring a lesion volume by drawing contours on many medical images is labor-intensive, poorly reproducible, and subjective. An automated volume measurement scheme has the potential to improve efficiency, consistency, and objectivity, avoiding problems of fatigue, variations in hand-eye coordination and subjective decision-making. Colon cancer is the second leading cause of cancer deaths in the United States (Jemal et al., 2008). In the screening for this disease, lesion size plays an especially important role in determining malignant potential and the need for intervention. In particular, tracking a change in polyp size is an important marker for the clinician. Computed tomography colonography (CTC) is a diagnostic examination that radiologists use for detecting colonic polyps (precursor of colon cancer) (Macari & Bini, 2005). Significantly, the measured size of a polyp at CTC directs clinical treatment, by determining whether results of screening require intervention (Pickhardt et al, 2003). For instance, the current clinical standard is to weigh polyps ≥ 10 mm more highly. However, current measurement of the single longest dimension of a polyp is subjective and has variations among radiologists. As evidence of the variability of manual linear measurement of polyps at CTC, studies reported interobserver and intraobserver variations between 16 to 40% (Yeshwant et al., 2006; Taylor et al., 2006; Jeong et al., 2008). As stated earlier, volume measurement could be more clinically informative than longest linear dimension, and this holds true in CTC. However, manual measurement of polyps at CTC suffers from the same inherent problems as lesions in general (labor intensity, poor reproducibility, and subjectivity), and similarly would benefit from automation. As a medical sign frequently occurring in the population, a consistent and 20

TH‐C‐304A‐10: Computer‐Aided Measurement of Liver Volumes in CT by Means of Fast‐Marching and Level‐Set Segmentation

Purpose: Measuring the liver volume by manual tracing of the liver boundary on arterial‐phase CTimages is time‐consuming. Our purpose was to develop an automated liver extraction scheme based on a 3D level‐set segmentation technique for measuring liver volumes. Material and Methods: Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol. We developed an automated liver segmentation scheme for volumetry of the liver in CT. Our scheme consisted of five steps. First, a 3D anisotropic smoothing filter was applied to CTimages for removing noise while preserving the structures in the liver, followed by an edge enhancement filter and a nonlinear gray‐scale enhancement filter for enhancing the liver boundary. By using the boundary‐enhanced image as a speed function, a 3D fast‐marching algorithm generated an initial surface that roughly estimated the shape of the liver. A 3D level‐set segmentation algorithm refined the initial surface so as to fit the liver boundary more accurately. Automated volumes were compared to manually determined liver volumes. Results: The mean liver volume obtained with our scheme was 1598 cc (range: 1002–2415 cc), whereas the mean manual volume was 1535 cc (range: 1007–2435 cc). The mean absolute difference between automated and manual volumes was 128 cc (9.5%). The two volumetrics reached an excellent agreement (the intra‐class correlation coefficient was 0.89) with no statistically significant difference (P=0.13). The processing time by the automated method was 2–5 min. per case (Intel, Xeon, 2.7 GHz), whereas that by manual segmentation was approximately 50–60 min. per case. Conclusion:CTliver volumetrics based on an automated scheme agreed excellently with manual volumetrics, and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes in CT; thus, it would be useful for radiologists in their measurement of liver volumes.