Algorithms for 3D reconstruction and calculation of object parameters based on the results of detection in medical images

The article presents algorithms for reconstruction, calculation of stone parameters and visualization of three-dimensional kidney and stone objects based on data obtained after the detection of 2D objects by a neural network on medical images obtained as a result of computed tomography of human internal organs. The algorithms allow you to restore (assemble) kidney and stone objects, calculate the physical parameters of stones, and perform flat and three-dimensional visualization of stones. The implementation of algorithms in the software code allows you to obtain the dimensions of the found concretions in the kidneys, visualize the density distribution inside the stone, visualize the location of the found stones in the kidney, which simplifies the support of medical decision-making during diagnosis and subsequent planning of surgical intervention during the stone crushing procedure using a laser installation. The proposed algorithms and models were implemented in a prototype of a medical decision support system in surgery and urology using computer vision technologies as part of software modules. The use of the developed algorithms for layered assembly of stones and kidneys in the prototype of a medical decision support system in surgery and urology using computer vision reduces the time for diagnosis and planning of stone crushing surgery, and also helps to avoid errors in determining the location of stones inside the kidney and, thereby, reduce the likelihood of injury to the patient.

1. Meldo A.A., Utkin L.V., Trofimova T.N. Artificial intelligence in medicine: current state and main directions of development of the intellectual diagnostics. Luchevaya diagnostika i terapiya = Diagnostic radiology and radiotherapy. 2020;(1):9–17. (In Russ.). https://doi.org/10.22328/2079-5343-2020-11-1-9-17

2. Nazarenko G.I., Guliev Ya.I., Ermakov D.E. Meditsinskie informatsionnye sistemy: teoriya i praktika. Moscow: FIZMATLIT; 2005. 320 p. (In Russ.).

3. Borisov D.N., Kul'nev S.V., Lemeshkin R.N. Ispol'zovanie iskusstvennogo intellekta pri analize tsifrovykh diagnosticheskikh izobrazhenii. In: State and prospects for the development of modern science in the field of «Technical vision and pattern recognition», 16-17 October 2019, Anapa, Russia. Anapa: Military Innovative Technopolis «ERA»; 2019. P. 163–169. (In Russ.).

4. Martov A.G., Mazurenko D.A., Klimkova M.M., Sinitsyn V.E., Nersisyan L.A. Dual energy computed tomography in diagnosis of urolithiasis: a new method for determining the chemical composition of urinary stones. Urologiya = Urologiia. 2017;(3):98–103. (In Russ.). https://doi.org/10.18565/urol.2017.3.98-103

5. Hidas G., Eliahou R., Duvdevani M. et al. Determination of Renal Stone Composition with Dual-Energy CT: In Vivo Analysis and Comparison with X-ray Diffraction. Radiology. 2010;257(2):394–401. https://doi.org/10.1148/radiol.10100249

6. Mackenzie A., Lewis E., Loveland J. Successes and challenges in extracting information from DICOM image databases for audit and research. British Journal of Radiology. 2023;96(1151). https://doi.org/10.1259/bjr.20230104

7. Sirisha U., Praveen S.Ph., Srinivasu P.N., Barsocchi P., Bhoi A.K. Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection. International Journal of Computational Intelligence Systems. 2023;16(1). https://doi.org/10.1007/s44196-023-00302-w

8. Rudenko M.A., Rudenko A.V., Krapivina M.A., Lisovsky V.S. System for detecting and analyzing objects on CT images in urology. In: III International Conference on Neural Networks and Neurotechnologies (NeuroNT'2022), 16 June 2022, Saint Petersburg, Russia. Saint Petersburg: Saint Petersburg Electrotechnical University «LETI»; 2022. P. 38–42. (In Russ.).

9. Rudenko A.V., Rudenko M.A., Kashirina I.L. Mechanism for evaluating the results of detection and classification of objects in medical images. Vestnik Voronezhskogo gosudarstvennogo universiteta. Seriya: Sistemnyi analiz i informatsionnye tekhnologii = Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies. 2024;(1):137–148. (In Russ.).

10. Chernega V.S., Tluchovskaya-Stepanenko N.P., Eremenko S.N., Eremenko A.N. The Network model for Estimation of duration laser contact lithotripsy. Vrach i informatsionnye tekhnologii = Medical Doctor and Information Technologies. 2018;(4):75–82. (In Russ.).

11. Chernega V.S., Eremenko S.N., Eremenko A.N., Tluhovska-Stepanenko N.P. Prediction of time of transurethral holmium lithotripsy in urolitiaz's treatment. Vrach i informatsionnye tekhnologii = Medical Doctor and Information Technologies. 2020;(2):72–80. (In Russ.). https://doi.org/10.37690/1811-0193-2020-2-72-80