Preview

Radiology - Practice

Advanced search

Automatic Batch Determining Radiodensity of the Liver to Detect Subclinical Liver Cases

Abstract

The paper proposes a system for automatic segmentation and determining radiodensity of the liver developed by the authors. Retrospective study of the system is performed. The system is able to correctly determine radiodensity of both normal liver and the liver with pathological changes, able to handle tomograms where the liver is presented partially. The system can be used for automatic determining radiodensity of the liver on large data bases of computed tomograms. It can be used for revealing subclinical cases of the liver as well as for research works.

About the Authors

N. S. Kulberg
Monitoring and Controlling Tools Design Department Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Healthсare Department
Russian Federation


A. B. Elizarov
Monitoring and Controlling Tools Design Department Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Healthсare Department
Russian Federation


V. P. Novic
Monitoring and Controlling Tools Design Department Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Healthсare Department
Russian Federation


V. A. Gombolevskiy
Monitoring and Controlling Tools Design Department Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Healthсare Department
Russian Federation


A. P. Gonchar
Monitoring and Controlling Tools Design Department Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Healthсare Department
Russian Federation


V. Yu. Bosin
Monitoring and Controlling Tools Design Department Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Healthсare Department
Russian Federation


A. V. Vladzimirskiy
Monitoring and Controlling Tools Design Department Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Healthсare Department
Russian Federation


S. P. Morozov
Monitoring and Controlling Tools Design Department Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Healthсare Department
Russian Federation


References

1. Кульберг Н. С., Елизаров А. Б., Ковбас В. С. Программа сегментации изображения печени и определения рентгеновской плотности печени CTLiverExam. Свидетельство о государственной регистрации программы для ЭВМ № 2019660983. 2019.

2. Усанов М. С., Кульберг Н. С., Морозов С. П. Опыт применения адаптивных гомоморфных фильтров для обработки компьютерных томограмм // Информационные технологии и вычислительные системы. 2017. № 2. С. 33-42.

3. Усанов М. С., Кульберг Н. С., Морозов С. П. Разработка алгоритма анизотропной нелинейной фильтрации данных компьютерной томографии с применением динамического порога // Компьютерные исследования и моделирование. 2019. Т. 11. № 2. С. 233-248. DOI: 10.20537/2076-7633-2019-11-2-233-248.

4. Choi S. H., Kwon H. J., Lee S. Y. et al. Focal hepatic solid lesions incidentally detected on initial ultrasonography in 542 asymptomatic patients // Abdom. Radiol. 2016. V. 41. P. 265-272. DOI: 10.1007/s00261-015-0567-9.

5. Collin P., Rinta-Kiikka I., Räty S., Laukkarinen J., Sand J. Diagnostic workup of liver lesions: Too long time with too many examinations // Scand. J. Gastroenterol. 2015. V. 50. P. 355-359. DOI: 10.3109/00365521.2014.999349.

6. Gore R. M., Thakrar K. H., Wenzke D. R., et al. That liver lesion on MDCT in the oncology patient: is it important? // Cancer Imag. 2012. V. 12. № 2. P. 373-384. DOI: 10.1102/1470-7330.2012.9028.

7. Gore R. M., Pickhardt P. J., Mortele K. J., Fishman E. K., Horowitz J. M., Fimmel C. J., Talamonti M. S., Berland L. L., Pandharipande P. V. Management of incidental liver lesions on ct: a white paper of the ACR incidental findings committee. j am coll radiol. 2017. V. 14. № 11. P. 1429-1437. DOI: 10. 1016/j.jacr.2017.07.018.

8. Graffy P. M., Sandfort V., Summers R. M., Pickhardt P. J. Automated Liver Fat Quantification at nonenhanced abdominal CT for population-based steatosis assessment // Radiol. 2019. Online. DOI: 10.1148/radiol.2019190512.

9. Huang Q., Ding H., Wang X., Wang G. Fully automatic liver segmentation in CT images using modified graph cuts and feature detection // Comput. Biol. Med. 2018. V. 95. P. 198-208. DOI: 10.1016/j.compbiomed.2018.02.012.

10. Kaltenbach T. E., Engler P., Kratzer W., Oeztuerk S., Seufferlein T., Haenle M. M., Graeter T. Prevalence of benign focal liver lesions: ultrasound investigation of 45,319 hospital patients // Abdom. Radiol. 2016. V. 41. № 1. P. 25-32. DOI: 10.1007/s00261-015-0605-7.

11. Maxwell A. W., Keating D. P., Nickerson J. P. Incidental abdominopelvic findings on expanded field-of-view lumbar spinal importance, and concordance in interpretation by neuroimaging and body imaging radiologists // Clin. Radiol. 2015. V. 70. № 2. P. 161-167. DOI: 10.1016/j.crad.2014.10.016.

12. Pickhardt P. J., Park S. H., Hahn L., Lee S. G., Bae K. T., Yu E. S. Specificity of unenhanced CT for non-invasive diagnosis of hepatic steatosis: Implications for the investigation of the natural history of incidental steatosis // Eur. Radiol. 2012. V. 22. № 5. P. 1075-1082. DOI: 10.1007/s00330-011-2349-2.

13. Quattrocchi C. C., Giona A., Di Martino A. C. et al. Extra-spinal incidental findings at lumbar spine MRI in the general population: a large cohort study // Insights Imaging. 2013. V. 4. № 3. P. 301-308. DOI: 10.1007/s13244-013-0234-z.

14. Spinczyk D., Krasoń A. Automatic liver segmentation in computed tomography using general-purpose shape modeling methods. 2018. BioMedical Engineering OnLine V. 17. № 65. DOI: 10.1186/s12938-018-0504-6.

15. Venkatesh S. K., Chandan V., Roberts L. R. Liver Masses: A Clinical, Radiologic, and Pathologic Perspective. Clin Gastroenterol Hepatol. 2014. V. 12. P. 1414-1429. DOI: 10.1016/j.cgh.2013.09.017.

16. Xuesong L., Qinlan X., Yunfei Zh., Defeng W. Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images. Scientific Reports. 2018. V. 8. № 10700. DOI: 10.1038/s41598-018-28787-y.

17. Yang D., Xu D., Zhou K. S., Georgescu B., Chen M., Grbic S., Metaxas D., Comaniciu D. Automatic Liver Segmentation Using an Adversarial Image-to-Image Network. In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer Assisted Intervention - MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science. 2017. V. 10435. Springer, Cham. DOI: 10.1007/978-3-319-66179-7_58.

18. Zeb I., Li D., Nasir K., Katz R., Larijani V. N., Budoff M. J. Computed Tomography Scans in the Evaluation of Fatty Liver Disease in a Population Based Study. The Multi-Ethnic Study of Atherosclerosis. Acad Radiol. 2012. V. 19. № 7. P. 811-818. DOI: 10.1016/j.acra.2012.02.022.


Review

For citations:


Kulberg N.S., Elizarov A.B., Novic V.P., Gombolevskiy V.A., Gonchar A.P., Bosin V.Yu., Vladzimirskiy A.V., Morozov S.P. Automatic Batch Determining Radiodensity of the Liver to Detect Subclinical Liver Cases. Radiology - Practice. 2020;(3):50-61. (In Russ.)

Views: 320


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2713-0118 (Online)