The frequency of moderate-to-severe hepatic steatosis in an asymptomatic population increases annually [1]. Today non-contrast computed tomography (CT) provides fast, reproducible and noninvasive hepatic steatosis quantification [2,3]. Liver segmentation can be done by semi-automatic and automatic methods. Semi-automatic methods can require a large amount of radiologist’s time for interactive segmentation [4, 5]. Attenuation-based algorithms, various clustering methods, pattern recognition, and neural networks are used for automated liver segmentation [6-10]. Despite the significant advances in this area, available algorithms have some weaknesses, mainly the lack of...

We randomly selected 60 unenhanced chest LDCT studies (30 males, 30 females aged 55-74 years) (Figure 1). as the algorithm would allow for detection of pre-clinical liver disorders in a large population. Manual liver density was measured with >100mm2 region of interes (ROI) in liver segments and than averaged. As liver density in various diseases varies from -40 HU to +100 HU [11], we deemed ROI-based density segmentation inaccurate and created an algorithm that takes liver shape into account. An algorithm for liver densitometry should...

The liver was successfully localized in all cases. The algorithm requires at least half of true liver volume for accurate segmentation and densitometry. The algorithm correctly identifies the position of the liver even in the following cases: the liver had been scanned partially (which is especially important for chest LDCT) diffuse pathological liver attenuation (i.e., in severe steatosis) (Fig. 5) focal liver lesions The program underestimated real liver dimensions. We attribute this deviation to shape variations between our model and real samples. Thus, improved liver...

To our knowledge, this is the first program for automatic liver density measurement. It could be used to free radiologist’s valuable time with more resources being directed towards complex or urgent cases. The algorithm may allow for fast, large scale automated CT analysis in screening and clinical trials.

[1] Arablinsky A.V., C. M. H. (2008). To a Question about Noninvasive Diagnostics of Fatty Degeneration of a Liver at Patients with non Alcoholic Fatty Hepatitis. http://vidar.ru/Article.asp?fid=MV_2008_1_46 [2] Kodama, Y., Ng, C. S., Wu, T. T., Ayers, G. D., Curley, S. A., Abdalla, E. K., … Charnsangavej, C. (2007). Comparison of CT methods for determining the fat content of the liver. American Journal of Roentgenology, 188(5), 1307–1312. https://doi.org/10.2214/AJR.06.0992 [3] Pickhardt, P. J., Park, S. H., Hahn, L., Lee, S. G., Bae, K. T., & Yu,...