Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm

Three-dimensional virtual planning for nodule resection in solid organs: A systematic review and meta-analysis

OBJECTIVES

To systematically review the effects of 3D-imaging virtual planning for nodule resection in the following solid organs: lung, liver, and kidney.

METHODS

MEDLINE, EMBASE, and Cochrane Library were searched through September 31, 2020 to include randomized and non-randomized controlled studies that compared outcomes of surgical resection of lung, liver, or kidney nodule resection with and without 3D virtual planning with computed tomography. From each article, the mean operation time (OT), mean estimated blood loss (EBL), mean postoperative hospital stay (POHS), and the number of postoperative events (POE) were extracted. The effect size (ES) of 3D virtual planning vs. non-3D planning was extracted from each study to calculate the pooled measurements for continuous variables (OT, EBL, POHS). Data were pooled using a random-effects model.

RESULTS

The literature search yielded 2397 studies and 10 met the inclusion criteria with a total of 897 patients. There was a significant difference in OT between groups with a moderate ES favoring the 3D group (ES,-0.56; 95%CI: 0.91,-0.29; I² = 83.1%; p < .001). Regarding EBL, there was a significant difference between 3D and non-3D with a small ES favoring IGS (ES,-0.18; 95%CI: 0.33,-0.02; I² = 22.5%; p = .0236). There was no difference between the 3D and non-3D groups for both POHS (POHS ES,-0.15; 95%CI: 0.39,0.10; I² = 37.0%; p = .174) and POE (POE odds ratio (OR),0.80; 95%CI:0.54,1.19; I² = 0.0%; p = .0.973).

CONCLUSIONS

3D-imaging planning for surgical resection of lung, kidney, and liver nodules could reduce OT and EBL with no effects on immediate POHS and POE. Improvements in these perioperative variables could improve medium and long-term postoperative clinical outcomes.

Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation

Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.

Tissue Structure Updating for In Situ Augmented Reality Navigation Using Calibrated Ultrasound and Two-Level Surface Warping

OBJECTIVE

In minimally invasive surgery (MIS), in situ augmented reality (AR) navigation systems are usually implemented using a glasses-free 3D display to represent the preoperative tissue structure, and can provide intuitive see-through guidance information. However, due to changes in intraoperative tissue, the preoperative tissue structure is not able to exactly correspond to reality, which influences the precision of in situ AR navigation. To solve this problem, we propose a method to update the tissue structure for in situ AR navigation in such way to reflect changes in intraoperative tissue.

METHODS

The proposed method to update the tissue structure is based on the calibrated ultrasound and two-level surface warping technologies. Firstly, the particle filter-based calibration is implemented to perform ultrasound calibration and obtain intraoperative position of anatomical points. Secondly, intraoperative positions of anatomical points are inputted in the two-level surface warping method to update the preoperative tissue structure. Finally, the glasses-free real 3-D display of the updated tissue structure is finished, and is superimposed onto a patient by a translucent mirror for in situ AR navigation.

RESULTS

we validated the proposed method by simulating liver tissue intervention, and achieved the tissue updating accuracy of 92.86%. Furthermore, the targeting error of AR navigation based on the proposed method was also evaluated through minimally invasive liver surgery, and the acquired mean targeting error was 1.92 mm.

CONCLUSION

The results demonstrate that the proposed AR navigation method is effective.

SIGNIFICANCE

The proposed method can facilitate MIS, as it provides accurate 3D navigation.

Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images

Medical image analysis plays an important role in computer-aided liver-carcinoma diagnosis. Aiming at the existing image fuzzy clustering segmentation being not suitable to segment CT image with non-uniform background, a fast robust kernel space fuzzy clustering segmentation algorithm is proposed. Firstly, the sample in euclidean space is mapped into the high dimensional feature space through the kernel function. Then the linear weighted filtering image is obtained by combining the current pixel with its neighborhood pixels through the space information in CT image. Finally, the two-dimensional histogram between the clustered pixel and its neighborhood mean is introduced into the robust kernel space image fuzzy clustering, and the iterative expression of the fast robust fuzzy clustering in kernel space is obtained by using Lagrange multiplier method. The experimental results on four databases show that our proposed method can segment liver tumors from abdominal CT volumes effectively and automatically, and the comprehensive segmentation performance of the proposed method is superior to that of several existing methods.

Domain-Based Analysis of Colon Polyp in CT Colonography Using Image-Processing Techniques

Background: The purpose of the research was to improve the polyp detection accuracy in CT Colonography (CTC) through effective colon segmentation, removal of tagged fecal matter through Electronic Cleansing (EC), and measuring the smaller polyps. Methods: An improved method of boundary-based semi-automatic colon segmentation with the knowledge of colon distension, an adaptive multistep method for the virtual cleansing of segmented colon based on the knowledge of Hounsfield Units, and an automated method of smaller polyp measurement using skeletonization technique have been implemented. Results: The techniques were evaluated on 40 CTC dataset. The segmentation method was able to delineate the colon wall accurately. The submerged colonic structures were preserved without soft tissue erosion, pseudo enhanced voxels were corrected, and the air-contrast layer was removed without losing the adjacent tissues. The smaller polyp of size less than <10mm was detected correctly. The results were statistically validated qualitatively and quantitatively. Segmented colons were validated through volumetric overlap computation, and accuracy of 95.826±0.6854% was achieved. In polyp measurement, the paired t-test method was applied to compare the difference with ground truth and at α=5%, t=0.9937 and p=0.098 was achieved. The statistical values of TPR=90%, TNR=82.3% and accuracy=88.31% were achieved. Conclusion: An automated system of polyp measurement has been developed starting from colon segmentation to improve the existing CTC solutions. The analysis of domain-based approach of polyp has given good results. A prototype software, which can be used as a low-cost polyp diagnosis tool, has been developed.

Up-to-date intraoperative computer assisted solutions for liver surgery

Computer assisted surgical planning allowed for a better selection of patients, evaluation of operative strategy, appropriate volumetric measurements, identification of anatomical risks, definition of tumour resection margins and choice of surgical approach in liver oncologic resections and living donor liver transplantations. Although preoperative computer surgical analysis has been widely used in daily clinical practice, intraoperative computer assisted solutions for risk analysis and navigation in liver surgery are not widely available or still under clinical evaluation. Computer science technology can efficiently assist modern surgeons during complex liver operations, mainly by providing image guidance with individualized 2D images and 3D models of the various anatomical and pathological structures of interest. Intraoperative computer assisted liver surgery is particularly useful in complex parenchyma-sparing hepatectomies, for intraoperative risk analysis and for the effective treatment of colorectal metastases after neoadjuvant therapy or when they are multiple. In laparoscopic liver surgery, intraoperative computer aid is definitively more important as, apart from a restricted field of view, there is also loss of the fine haptic feedback. Intraoperative computer assisted developments face challenges that prevent their application in daily clinical practice. There is a vast variety of studies regarding intraoperative computer assisted liver surgery but there are no clear objective measurements in order to compare them and select the most effective solutions. An overview of up-to-date intraoperative computer assisted solutions for liver surgery will be discussed.

3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.

Traditional surgical planning of liver surgery is modified by 3D interactive quantitative surgical planning approach: a single-center experience with 305 patients

BACKGROUND

Decision making and surgical planning are to achieve the precise balance of maximal removal of target lesion, maximal sparing of functional liver remnant volume, and minimal surgical invasiveness and therefore, crucial in liver surgery. The aim of this prospective study was to validate the accuracy and predictability of 3D interactive quantitative surgical planning approach (IQSP), and to evaluate the impact of IQSP on traditional surgical plans based on 2D images.

METHODS

A total of 305 consecutive patients undergoing hepatectomy were included in this study. Surgical plans were created by traditional 2D approach using picture archiving and communication system (PACS) and 3D approach using IQSP respectively by two groups of physicians who did not know the surgical plans of the other group. The two surgical plans were submitted to the chief surgeon for selection before operation. The specimens were weighed. The two surgical plans were compared and analyzed retrospectively based on the operation results.

RESULTS

The two surgical plans were successfully developed in all 305 patients and all the 3D IQSP surgical plans were selected as the final decision. Total 278 patients successfully underwent surgery, including 147 uncomplex hepatectomy and 131 complex hepatectomy. Twenty-seven patients were withdrawn from hepatectomy. In the uncomplex group, the two surgical plans were the same in all 147 patients and no statistically significant difference was found among 2D calculated resection volume (2D-RV), 3D IQSP calculated resection volume (IQSP-RV) and the specimen volume. In the complex group, the two surgical plans were different in 49 patients (49/131, 37.4%). According to the significance of differences, the 49 different patients were classified into three grades. No statistically significant difference was found between IQSP-RV and specimen volume. The coincidence rate of territory analysis of IQSP with operation was 92.1% (93/101) for 101 patients of anatomic hepatectomy.

CONCLUSIONS

The accuracy and predictability of 3D IQSP were validated. Compared with traditional surgical planning, 3D IQSP can provide more quantitative information of anatomic structure. With the assistance of 3D IQSP, traditional surgical plans were modified to be more radical and safe.

Computer-assisted surgery planning in children with complex liver tumors identifies variability of the classical Couinaud classification

BACKGROUND

In complex malignant pediatric liver tumors there is an ongoing discussion regarding surgical strategy; for example, primary organ transplantation versus extended resection in hepatoblastoma involving 3 or 4 sectors of the liver. We evaluated the possible role of computer-assisted surgery planning in children with complex hepatic tumors.

METHODS

Between May 2004 and March 2016, 24 Children with complex liver tumors underwent standard multislice helical CT scan or MRI scan at our institution. Imaging data were processed using the software assistant LiverAnalyzer (Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany). Results were provided as Portable Document Format (PDF) with embedded interactive 3-dimensional surface mesh models.

RESULTS

Median age of patients was 33months. Diagnoses were hepatoblastoma (n=14), sarcoma (n=3), benign parenchyma alteration (n=2), as well as hepatocellular carcinoma, rhabdoid tumor, focal nodular hyperplasia, hemangioendothelioma, or multiple hepatic metastases of a pancreas carcinoma (each n=1). Volumetry of liver segments identified remarkable variations and substantial aberrances from the Couinaud classification. Computer-assisted surgery planning was used to determine surgical strategies in 20/24 children; this was especially relevant in tumors affecting 3 or 4 liver sectors. Primary liver transplantation could be avoided in 12 of 14 hepaoblastoma patients who theoretically were candidates for this approach.

CONCLUSIONS

Computer-assisted surgery planning substantially contributed to the decision for surgical strategies in children with complex hepatic tumors. This tool possibly allows determination of specific surgical procedures such as extended surgical resection instead of primary transplantation in certain conditions.