Local incompressible registration for liver ablation surgery assessment

Fu, Tianyu; Li, Qin; Liu, Dingkun; Ai, Danni; Song, Hong; Liang, Ping; Wang, Yongtian; Yang, Jian

doi:10.1002/mp.12535

Cited by 15 publications

(12 citation statements)

References 33 publications

(80 reference statements)

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“…Registering the pre-and post-ablation images has been a promising alternative to conventional side-by-side assessment for AMs, which has many advantages as follows (21)(22)(23): (1) faster and more accurate measurement of safety boundaries and (2) clearer observation of the spatial relationship between the tumor and ablation zone. Therefore, an increasing number of registration methods have emerged for evaluating AMs.…”

Section: Discussionmentioning

confidence: 99%

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

Jiang

Huang

et al. 2020

Front. Oncol.

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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 24month 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. An et al. Using Deep Learning for Registration 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.

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Section: Discussionmentioning

confidence: 99%

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

Jiang

Huang

et al. 2020

Front. Oncol.

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“…To address these problems, an automatic multi‐resolution framework for incompressible nonrigid registration with sliding motion compensation (I‐SMC) is proposed. In the present study, we extend our previous method to accurately register the images with large sliding motion. We search for the solution within a subspace of diffeomorphisms instead of searching for a solution in the complete space of nonparametric spatial transformations.…”

Section: Introductionmentioning

confidence: 92%

“…Furthermore, the soft tissues in the abdominal are considered as incompressible organs due to high water content . Thus, maintaining the volume of a soft tissue during nonrigid registration is important in the respiratory estimation for an accurate assessment of minimally invasive abdominal surgery . The existing incompressible nonrigid registration algorithms can be divided into the following three categories:…”

Section: Introductionmentioning

confidence: 99%

Nonrigid registration for tracking incompressible soft tissues with sliding motion

Liu

Wang

et al. 2019

Medical Physics

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Purpose: Respiration causes the deformation and sliding motion of the soft tissues, and affects the accuracy of the assessment of minimally invasive abdominal surgery. Nonrigid registration is used to eliminate the effects of respiration for the assessment. Because the soft tissues with high water content are volume preserving during deformation, incompressibility has to be considered when tracking soft tissues for nonrigid registration. The purpose of the study was to develop an incompressible nonrigid registration for tracking deformable soft tissues with sliding motion. Methods: The nonrigid registration framework proposed in the present study includes two main steps: (a) The solution in the subspace of diffeomorphisms is searched and encoded to stationary velocity field in the log domain. (b) The divergence-free component and harmonic remainder are extracted by Fourier-based Helmholtz-Hodge decomposition (FHHD) and further integrated by an adaptive weight to simultaneously retain the incompressibility of deformation and compensate the sliding motion. The method was evaluated on 11 groups of synthetic datasets and five groups of clinical images. Registration accuracy is evaluated by using four quantitative measures, including mean surface distance (MSD), Hausdorff distance (HD), mean corresponding distance (MCD), and Dice similarity coefficient (DSC). Incompressibility is evaluated by using two quantitative measures, including relative volume change (RVC) and Jacobian determinant (J). Results: Compared with three state-of-the-art nonrigid registration methods, the proposed method shows an advantage in handling the incompressible deformation of images with large sliding motion. The lowest (MSD, 0.631 mm), (HD, 6.000 mm), and (MCD, 3.555 mm) and the highest (DSC, 0.970) are obtained proving the high registration accuracy with sliding motion compensation of the proposed method. The (RVC, 0.006) and Jacobian determinant (J, 1.008 AE 0.070) are nearly close to 0 and 1, respectively, showing the strong incompressibility of the proposed method. The proposed method improves registration accuracy in nearly all cases, which maintains the incompressibility of tissue transformation while simultaneously compensating the sliding motion on clinical datasets. Conclusions: The proposed method improves the registration accuracy of incompressible tissues when dealing with large sliding motion, and thus has the potential to improve current minimally invasive abdominal surgery.

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“…5 On the contrary, the risk of tumor recurrence may increase if the resection area is small. 6 Therefore, the understanding of the hepatic anatomy to decide the optimal resection area is significant before the operation. The hepatic anatomy could be described in the morphological and functional aspects.…”

Section: Introductionmentioning

confidence: 99%

“…If the resection area is large, then the surrounding vessel may be damaged and the recovery of the patient is difficult 5 . On the contrary, the risk of tumor recurrence may increase if the resection area is small 6 . Therefore, the understanding of the hepatic anatomy to decide the optimal resection area is significant before the operation.…”

Section: Introductionmentioning

confidence: 99%

Multiple feature‐based portal vein classification for liver segment extraction

Gao

et al. 2021

Medical Physics

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The liver segments divided by Couinaud classification method are used to understand the functional anatomy of liver, which is significant in hepatic resection surgery. In Couinaud classification method, each third-order branch of the portal vein (PV) defines the supplied territory of a corresponding liver segment. However, the accuracies of the reconstruction and classification of PV are affected by the complicated structure of the vein. The purpose of this paper is to develop a separation and classification method that can accurately extract the liver segments. Methods: In this paper, a multiple feature-based method is proposed to obtain liver segments. Because the portal and hepatic veins usually connect in the vessel segmentation result, the PV is first completely separated based on the different strategies for minimal node cut using fused features of topology and appearance. Meanwhile, all bifurcation nodes of PV are detected. The bifurcation nodes are initial ordered through their linkages to classify the branches. Then, the feature of the vascular topology is used to refine the orders of bifurcation nodes. The bifurcation nodes with the refined orders classify the branches between them, and the third-order branches of PV are obtained. The liver segments are eventually obtained through the third-order branches. Results: The separation and classification in the proposed method are evaluated on the CT and MR datasets. The average values of Dice, Jaccard, Recall, and Precision obtained by the proposed method are 93.00%, 87.90%, 93.47%, and 93.19%, respectively. Compared with the state-of-the-art methods, the separation results obtained by the proposed method are more accurate. The branches of PV are classified based on the separation result. According to the third-order branches, eight liver segments correspond to the different functional areas are precisely extracted. Conclusions: The proposed method achieves a high accuracy for the liver segment extraction. And the extracted liver segments are significant for the preplanning of resection surgery.

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Local incompressible registration for liver ablation surgery assessment

Abstract: The proposed method achieves a high accuracy for registering the livers. The sizes and positions of the ablation area and tumor are accurately compared for the assessment of ablation surgery.

Cited by 15 publications

References 33 publications

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

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

Nonrigid registration for tracking incompressible soft tissues with sliding motion

Multiple feature‐based portal vein classification for liver segment extraction

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