Target registration error minimization for minimally invasive interventions involving deformable organs

Target registration error reduction for percutaneous abdominal intervention

A real-time methodology that finds spatio-temporal correspondence between the positions of the target point in the pre-treatment 3DCT image and during the procedure was proposed. It based on minimizing the target registration error in III tier registration circuits. Particle Swarm Optimization and Differential Evaluation were used to find optimal values of Elastic Body Spline parameters in the generation of abdominal deformation field. Different transformation classes have been tested: rigid, affine, Thin Plate Spline, Elastic Body Spline. The lowest TRE was obtained for the swarm optimization algorithm - differential evolution for the rigid and affine version: 3.47 and 3.73 mm, respectively.

Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration

Aim: To assess the ablative margin (AM) after microwave ablation (MWA) for hepatocellular carcinoma (HCC) with a deep learning-based deformable image registration (DIR) technique and analyze the relation between the AM and local tumor progression (LTP).

Patients and Methods: From November 2012 to April 2019, 141 consecutive patients with single HCC (diameter ≤ 5 cm) who underwent MWA were reviewed. Baseline characteristics were collected to identify the risk factors for the determination of LTP after MWA. Contrast-enhanced magnetic resonance imaging scans were performed within 1 month before and 3 months after treatment. Complete ablation was confirmed for all lesions. The AM was measured based on the margin size between the tumor region and the deformed ablative region. To correct the misalignment, DIR between images before and after ablation was achieved by an unsupervised landmark-constrained convolutional neural network. The patients were classified into two groups according to their AMs: group A (AM ≤ 5 mm) and group B (AM > 5 mm). The cumulative LTP rates were compared between the two groups using Kaplan–Meier curves and the log-rank test. Multivariate analyses were performed on clinicopathological variables to identify factors affecting LTP.

Results: After a median follow-up period of 28.9 months, LTP was found in 19 patients. The mean tumor and ablation zone sizes were 2.3 ± 0.9 cm and 3.8 ± 1.2 cm, respectively. The mean minimum ablation margin was 3.4 ± 0.7 mm (range, 0–16 mm). The DIR technique had higher AUC for 2-year LTP without a significant difference compared with the registration assessment without DL (P = 0.325). The 6-, 12-, and 24-month LTP rates were 9.9, 20.6, and 24.8%, respectively, in group A, and 4.0, 8.4, and 8.4%, respectively, in group B. There were significant differences between the two groups (P = 0.011). Multivariate analysis showed that being >65 years of age (P = 0.032, hazard ratio (HR): 2.463, 95% confidence interval (CI), 1.028–6.152) and AM ≤ 5 mm (P = 0.010, HR: 3.195, 95% CI, 1.324–7.752) were independent risk factors for LTP after MWA.

Conclusion: The novel technology of unsupervised landmark-constrained convolutional neural network-based DIR is feasible and useful in evaluating the ablative effect of MWA for HCC.

Virtual reality with three-dimensional image guidance of individual patients' vessel anatomy in laparoscopic distal pancreatectomy

PURPOSE

Three-dimensional virtual endoscopy (3DVE) has the potential advantage of enhanced anatomic delineation and spatial orientation during laparoscopic procedures. In the present study, we aimed to evaluate the impact of 3DVE guidance in laparoscopic distal pancreatectomy (LDP).

METHODS

Thirty-eight patients presenting to our hospital with a variety of pancreatic tumors underwent preoperative computed tomography scanning to clearly define the major peripancreatic vasculature and correlate it with a 3DVE system (SYNAPSE VINCENT: Fujifilm Medical, Tokyo, Japan). This map served as the guide during preoperative planning, surgical education, and simulation and as intraoperative navigation reference for LDP. Operative records and pathological findings were analyzed for each procedure. Operative parameters were compared between the 38 patients in this study and 8 patients performed without 3DVE guidance at our institution.

RESULTS

The 3DVE navigation system successfully created a preoperative resection map in all patients. Relevant peripancreatic vasculature displayed on the system was identified and compared during the intervention. The mean blood loss in LDP performed under 3DVE guidance versus LDP without 3DVE was 168.5 +/- 347.6 g versus 330.0 +/- 211.4 g, p = 0.008 while and the operative time was 171.9 +/- 51.7 min versus 240.6 +/- 24.8 min, p = 0.001.

CONCLUSIONS

3DVE in conjunction with a "laparoscopic eye" creates a preoperative and intraoperative three-dimensional data platform that potentially enhances the accuracy and safety of LDP.

An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions

PURPOSE

The registration of preoperative 3D images to intra-operative laparoscopic 2D images is one of the main concerns for augmented reality in computer-assisted surgery. For laparoscopic liver surgery, while several algorithms have been proposed, there is neither a public dataset nor a systematic evaluation methodology to quantitatively evaluate registration accuracy.

METHOD

Our main contribution is to provide such a dataset with an in vivo porcine model. It is used to evaluate a state-of-the-art registration algorithm that is capable of simultaneous registration and soft-body collision reasoning.

RESULTS

The dataset consists of 13 deformed liver states, with corresponding exploration videos and interventional CT acquisitions with 60 small artificial fiducials located on the surface of the liver and distributed within the parenchyma, where a precise registration is crucial for augmented reality. This dataset will be made public. Using this dataset, we show that collision reasoning improves performance of registration for strong deformation and independent lobe motion.

CONCLUSION

This dataset addresses the lack of public datasets in this field. As an example of use, we present and evaluate a state-of-the-art energy-based approach and a novel extension that handles self-collisions.

Selection of the respiratory phase in minimally invasive interventions for target registration error minimization

BACKGROUND

In minimally invasive surgery, the main challenge is precisely locating the target during the intervention. For abdominal intervention, one of most important factors causing target motion is breathing. The aim of the study is to efficiently predict target localization during the respiratory in breathing cycle.

METHOD

Analysis of target registration error (TRE) for the registration circuits method was used to find the breathing phase corresponding to the preoperative Computed Tomography spatial configuration. Then, Elastic Body Spline (EBS) for modeling deformation field and Particle Swarm Optimization method were used to find the desired values of EBS parameters: ∝ and stiffness were used.

RESULTS

The proposed methodology was verified during experiments conducted on 21 patients diagnosed with liver tumors. This ability of TRE reduction has been achieved for the respiratory phases founded in registration chain analysis.

CONCLUSIONS

The proposed method presents the usability of spatio-temporal analysis of collected real breathing data in order to estimate the position of a target during the respiratory cycle. This method has been developed to perform operations in real-time on a standard workstation.

Automatic liver segmentation in computed tomography using general-purpose shape modeling methods

BACKGROUND

Liver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods.

METHODS

As part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field.

RESULTS

Mutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55[Formula: see text], of which for three of them the coefficient was over 70[Formula: see text], which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5[Formula: see text] CONCLUSIONS: This value of 88.5 [Formula: see text] Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object-the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the model can be modeled mathematically by using various kernel functions, which allows to segment the liver on a comparable level using a smaller learning set.