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. 2020 Nov 12;9(11):397.
doi: 10.3390/biology9110397.

Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma

Corina Radu  1   2 , Petra Fisher  1 , Delia Mitrea  3 , Iosif Birlescu  3 , Tiberiu Marita  3 , Flaviu Vancea  3 , Vlad Florian  3 , Cristian Tefas  1   2 , Radu Badea  2 , Horia Ștefănescu  1 , Sergiu Nedevschi  3 , Doina Pisla  3 , Nadim Al Hajjar  1   2
Affiliations

Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma

Corina Radu et al. Biology (Basel). .

Abstract

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with its mortality rate correlated with the tumor staging; i.e., early detection and treatment are important factors for the survival rate of patients. This paper presents the development of a novel visualization and detection system for HCC, which is a composing module of a robotic system for the targeted treatment of HCC. The system has two modules, one for the tumor visualization that uses image fusion (IF) between computerized tomography (CT) obtained preoperatively and real-time ultrasound (US), and the second module for HCC automatic detection from CT images. Convolutional neural networks (CNN) are used for the tumor segmentation which were trained using 152 contrast-enhanced CT images. Probabilistic maps are shown as well as 3D representation of HCC within the liver tissue. The development of the visualization and detection system represents a milestone in testing the feasibility of a novel robotic system in the targeted treatment of HCC. Further optimizations are planned for the tumor visualization and detection system with the aim of introducing more relevant functions and increase its accuracy.

Keywords: hepatocellular carcinoma; image fusion; robotic-assisted treatment; targeted treatment; ultrasound.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
ProHep-LCT robotic system: (a) robotic system in “mirrored” configuration; (b) one module guiding of the robotic system.
Figure 2
Figure 2
Automated instruments for the ProHep-LCT robotic system: (a) needle insertion instrument; (b) US probe manipulation instrument; (c) Hitachi Arieta intraoperatory US probe.
Figure 2
Figure 2
Automated instruments for the ProHep-LCT robotic system: (a) needle insertion instrument; (b) US probe manipulation instrument; (c) Hitachi Arieta intraoperatory US probe.
Figure 3
Figure 3
The implementation of the tumor visualization and detection system into the robotic system (general scheme).
Figure 4
Figure 4
The probabilistic maps and the ground truth for the automatic hepatocellular carcinoma (HCC) segmentation within contrast-enhanced computerized tomography (CT) images: (a) the original images with the tumors depicted by the radiologists (the ground truth); (b) the automatically generated probabilistic maps (the red pixels denote an increased probability for the pixels that belong to the HCC class, while the blue pixels denote a low probability for the pixels that do not belong to this class).
Figure 5
Figure 5
The 3D model of the HCC tumor within the liver (right); the corresponding axial, coronal and sagittal slices with the depicted HCC tumor emphasized (left).
Figure 6
Figure 6
3D representation of the HCC tumor and of the main blood vessels (right); the corresponding random angle 2D sections with the HCC tumor emphasized (left).
See this image and copyright information in PMC

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