KEYWORDS: Kidney, Tumors, Image segmentation, Data modeling, Computed tomography, 3D scanning, Tumor growth modeling, 3D modeling, Surgery, Health sciences
CT-guided renal tumor ablations have been considered an alternative to treat small renal tumors, typically 4 cm in size or smaller, especially for patients who are ineligible to receive nephron-sparing surgery. For this procedure, the radiologist must compare the pre-operative with the post-operative CT to determine the presence of residual tumors. Distinguishing between malignant and benign kidney tumors poses a significant challenge. To automate this tumor coverage evaluation step and assist the radiologist in identifying kidney tumors, we proposed a coarse-to-fine U-Net-based model to segment kidneys and masses. We used the TotalSegmentator tool to obtain an approximate segmentation and region of interest of the kidneys, which was inputted into our 3D segmentation network trained using the nnUNet library to fully segment the kidneys and masses within them. Our model achieved an aggregated DICE score of 0.777 on testing data, and on local CT kidney data with tumors collected from the London Health Sciences University Hospital, our model achieved a DICE score of 0.7 for tumour segmentation. Our results indicate the model will be useful for tumour identification and evaluation.
Percutaneous thermal ablations are promising curative treatment techniques of focal liver tumors, particularly for those patients who are not eligible for surgical resection. Complete coverage of the targeted tumor by the thermal ablation zone and with a safety margin of 5-10 mm is required to ensure that complete tumor eradication will be achieved. 2D ultrasound (US) is a commonly used modality to guide this procedure; however, it has limitations in estimating the ablation tumor coverage due to the difficulty of evaluating tumor coverage using only one or multiple 2D US images. The use of intra-procedural 3D US is a promising approach to solve this unmet need. Although most of current approaches provide reformatted three orthogonal views to better evaluate the tumor coverage, comprehensive volumetric evaluation is rarely available. In this paper, for tumor-visible cases in US, we aim to investigate the ability of 3D US images to visualize the applicators and relevant surrounding structures, then assess the feasibility of evaluating the tumor coverage quantitatively using surface- and volume-based metrics. Using our previously developed 3D US liver ablation system, we collected 10 patients’ 3D US liver images in our clinical trial. The visibility of the applicator and relevant structures were assessed qualitatively. We then evaluated the surface error and volume accuracy of the tumor coverage. Results demonstrated that 3D US images allow visualization of the appropriate anatomical structures and applicators, and our volumetric evaluation can provide systematic knowledge of tumor coverage and an opportunity to correct the ablation applicator position intra-procedurally.
Image-guided percutaneous thermal ablations are promising techniques for the treatment of focal liver tumors. Conventionally, 2D ultrasound (US)-guidance is used extensively to assist liver tumor treatment. Recently, 3D US imaging has attracted much attention as its provided volumetric information can better help physicians interpret and localize liver structures. However, 3D US imaging still has the same limitation as conventional 2D US imaging in visualizing ultrasonically invisible cases. To address this issue, the current mainstream solution is to provide real-time 2D US-CT/MRI registered images by leveraging external tracking systems, such as electromagnetic (EM) or optical approaches. Due to their inevitable constraints, such as the presence of ferromagnetic structures in the case of EM tracking systems, or the line-of-sight limitation for optical systems, whether these solutions can be readily applied to the clinic has been under investigation. In this paper, we aim to investigate the feasibility of a 3D US-based ablation paradigm using our developed 2D/3D US/CT-guided liver ablation system. To achieve this goal and provide accurate guidance for ablation procedures, we proposed a local-and- global calibration method to track our mechatronic guidance arm. We also used a fiducial-based registration method to align 3D US with diagnostic CT images and implemented the re-slicing function to display the CT image corresponding to the US transducer’s pose. Results demonstrated the feasibility of our system to visualize the complementary information from multiple image modalities in real time. Our calibration method can provide accurate tracking with an unsigned error of 1.6 mm ± 0.4 mm. This work is a step towards providing a system to guide the liver ablation procedure, including cases with ultrasonically invisible or poorly visible tumors.
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