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Authors

Eleonora Tagliabue, Marco Piccinelli, Diego Dall’Alba, Juan Verde, Micha Pfeiffer, Riccardo Marin, Stefanie Speidel, Paolo Fiorini, Stephane Cotin

Abstract

Patient-specific Biomechanical Models (PBMs) can enhance computer assisted surgical procedures with critical information. Although pre-operative data allow to parametrize such PBMs based on each patient’s properties, they are not able to fully characterize them. In particular, simulation boundary conditions cannot be determined from preoperative modalities, but their correct definition is essential to improve the PBM predictive capability. In this work, we introduce a pipeline that provides an up-to-date estimate of boundary conditions, starting from the pre-operative model of patient anatomy and the displacement undergone by points visible from an intra-operative vision sensor. The presented pipeline is experimentally validated in realistic conditions on an ex vivo pararenal fat tissue manipulation. We demonstrate its capability to update a PBM reaching clinically acceptable performances, both in terms of accuracy and intra-operative time constraints.

Link to paper

DOI: https://doi.org/10.1007/978-3-030-87202-1_36

SharedIt: https://rdcu.be/cyhQy

Link to the code repository

https://gitlab.com/altairLab/banet

Link to the dataset(s)

N/A

Reviews

Review #1

Please describe the contribution of the paper

The study addresses an intraoperative update of boundary conditions (BCs) (it appears that the Authors focus solely on essential or Dirichlet BCs) for patient-specific computational biomechanics models for surgical guidance/planning from sparse/limited intraoperative information about the organ surface deformation. Deep Neural Network (DNN) is used for the boundary conditions update. The proposed framework is validated against the tissue deformation obtained through ex vivo pararenal tissue manipulation (grasping and pulling the tissue).
Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.

Appropriate identification and imposition of boundary conditions are crucial for computational biomechanics models to provide plausible/accurate prediction of organ deformations and force during surgery. Unfortunately, identification/determining of boundary conditions has attracted little attention by computational biomechanics community so far. The study undresses this important shortcoming of state-of-the-art of patient-specific computational biomechanics modelling by proposing a framework for determining boundary conditions during surgery. The proposed framework (Fig. 1) appears to be logical and, in principle, capable of providing accurate prediction of changing (during surgery) boundary conditions.
Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.

Abstract and Introduction refer to boundary conditions in a rather broad context (including forces between surgical instruments and tissues). However, the study focus solely on organ geometry and intraoperative deformations (i.e. essential or Dirichlet boundary conditions). Description of the methods used is very brief (bordering vague). It is unclear how mapping from the grid space to the computational biomechanics model space was done. Finite element FE simulation is mentioned in Section 3. However, there is no information how the FE analysis was conducted (number of elements and nodes, element type, analysis type: static or transient, linear of non-linear etc., FE solver/code used). Interpretation of the results seems too general. 3 mm accuracy for 25 mm deformation can be sufficient for some surgical procedures, but certainly not for surgical procedures in general (it would not be sufficient for neurosurgery).
Please rate the clarity and organization of this paper

Very Good
Please comment on the reproducibility of the paper. Note, that authors have filled out a reproducibility checklist upon submission. Please be aware that authors are not required to meet all criteria on the checklist - for instance, providing code and data is a plus, but not a requirement for acceptance

There is no reaosn to doubt in reproducibility of the presented results. However, making the code and models used in evaluation of the proposed methods publically available would help to gain more confidence in the study and benefit the research community (there are not many examples of determining the boundary conditions for surgery simulation from the intraoperative data).
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html

The Authors should be more clear about the scope and context of the study (essential boundary conditions, accuracy sufficient for only some surgical procedures). More information about the finite element analysis conducted is needed: number of elements and nodes, element type, analysis type: static or transient, linear of non-linear etc., FE solver/code used. The manuscript text is currently slightly shorter than 8 pages limit. There appears to be sufficient space to add more technical information about the finite element analysis conducted in the study and more references (if needed).
Please state your overall opinion of the paper

accept (8)
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

I would argue for accepting this manuscript: 1) It addresses a very important, but so far rarely addressed, topic of intraoperative determination/update of boundary conditions for patient-specific computational biomechanics models for surgery planning/surgical guidance. 2) There appears to be no major technical flaws in the study. 3) The weaknesses are in the introduction (defining the context and scope of the study) and insufficient information about the methods used. It appears that there is sufficient “free space” (within 8-pages limit) to add such information when revising the information. 4) The manuscript is clearly written. Figures are self-explanatory.
What is the ranking of this paper in your review stack?

1
Number of papers in your stack

2
Reviewer confidence

Very confident

Review #2

Please describe the contribution of the paper

The authors presented a pipeline where patient-specific biomechanical models may be updated for surgical assistance with data acquired via an intraoperative sensor. The details of the pipeline were described, as well as experiments and validations involving synthetic and real adipose tissues.
Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
1. A strategy that can effectively and efficiency augment patient-specific biomechanical modeling approach in assisting surgery is highly desirable. The validation results, as well as the execution speed/performance reported by the authors, are promising and welcoming developments.
2. The paper is well-organized and well-written. The figures are well-done.
3. The validation study is well-designed that includes synthetic data as well as physical experimental setup.
Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.
1. One possible improvement to the validation work is to increase the number of operators/participants (currently n =1) for the experiment involving real adipose tissue manipulation. While encouraging, some results in Table 1 may indicate the need to increase the study population to further investigate and understand the trend/impact of increased deformation levels (e.g. Grasp B State 3 from 20 mm to 30 mm to 40 mm).
2. Another possible improvement to the paper could be some additional details for the intraoperative data acquisition. In the current work the authors stated the use of Intel RealSense D435 RGB-D camera, and possibly with stereo-endoscope in the future. Some discussions and details on the setup, possible intraoperative burdens (time required to acquire/process data), should be included as prior works using patient-specific biomechanical models seem to indicate intraoperative data acquisition could be a rate-limiting step.
Please rate the clarity and organization of this paper

Excellent
Please comment on the reproducibility of the paper. Note, that authors have filled out a reproducibility checklist upon submission. Please be aware that authors are not required to meet all criteria on the checklist - for instance, providing code and data is a plus, but not a requirement for acceptance

The methodology used two algorithms/programs, namely ZoomOut and BANet. The former the authors provided a reference but did not make an explicit mention of the availability of its implementation. The latter the authors stated the paper used the publicly available implementation with pre-trained weights. The overall methodology of the paper should be reproducible with some efforts.
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html
1. In Fig. 1, a number of abbreviations (e.g. IOS) appears before it is explained in the body of the text.
2. It would be interesting to include the Dice coefficient for completeness in Fig 3 caption.
3. In Discussion and Conclusion, the authors stated that “By providing an update of model BCs with a very short delay, our method can handle situations with dynamically changing BCs, for example involving dissection, sutures removal or topological changes.” While the results here are encouraging, it might be more appropriate to scope/qualify this conclusion as this study only provide some initial support/evidence for dissection.
Please state your overall opinion of the paper

accept (8)
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

The authors presented an interesting and unique approach to augment intraoperative utilization of patient specific biomechanical modeling. The validation results were impressive, both in quantitative metrics comparing to accuracy levels referenced in prior literature, as well as execution speed considering the possible intraoperative burdens associated with computer-assisted surgery.
What is the ranking of this paper in your review stack?

1
Number of papers in your stack

3
Reviewer confidence

Confident but not absolutely certain

Review #3

Please describe the contribution of the paper

This work presented a complete pipeline that allows updating a patient-specific pre-operative model for surgical assistance, based on data acquired during the intervention.
Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.

The presented pipeline is experimentally validated in realistic conditions on an ex vivo pararenal fat tissue manipulation.
Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.

Lacks of comparison with other methods.
Please rate the clarity and organization of this paper

Satisfactory
Please comment on the reproducibility of the paper. Note, that authors have filled out a reproducibility checklist upon submission. Please be aware that authors are not required to meet all criteria on the checklist - for instance, providing code and data is a plus, but not a requirement for acceptance

difficult
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html

In particular, simulation boundary conditions cannot be determined from preoperative modalities, but their correct definition is essential to improve the PBM predictive capability.

1.This pipeline that provides an up-to-date estimate of boundary conditions, for large deformation, Nonlinear FEM should be implemented. Which Nonlinear FEM is used in this pipeline.

2.please give the Error analysis for the displacement estimation in the model.

3.For the Voxelization step, how to solve the edge of the Irregular objects? that’s very important for this study.

4.How to define the ground truth?

5.What is the type of mesh elements? Hexahedron or tetrahedron? why? what is the difference in the results between them?

6.Lacks of comparison with other methods. Only 1 table is not enough.
Please state your overall opinion of the paper

probably reject (4)
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

the reproducibility of the paper.
What is the ranking of this paper in your review stack?

2
Number of papers in your stack

4
Reviewer confidence

Confident but not absolutely certain

Primary Meta-Review

Please provide your assessment of this work, taking into account all reviews. Summarize the key strengths and weaknesses of the paper and justify your recommendation. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. In case of an invitation for rebuttal, clarify which points are important to address in the rebuttal.

Many technical details of the proposed method were missed by the two reviewers such as how to map the grid space the biomechanics model and how to acquire validation data. The revision should address those questions indeed.
What is the ranking of this paper in your stack? Use a number between 1 (best paper in your stack) and n (worst paper in your stack of n papers).

2

Author Feedback

We would like to thank the reviewers for their comments and the time dedicated to the revision of our contribution. In the following, we address the reviewers’ main concerns.

Information about the used methods One reviewer observes that the description of the methods appears brief. In this work, we have focused on the experimental validation of the presented pipeline in realistic conditions. This motivates the lack of in-depth information about the method. For a detailed description of all the methods employed in this work, readers should refer to references [Tagliabue, 2021] and [Melzi, 2019] of the manuscript.

Finite element method We agree with the reviewers that the paper would benefit from further details about the finite element method used for the experimental validation. Such details will be included in the final version of the manuscript. As described in Section 3, the pararenal adipose tissue has been modelled as a St Venant Kirchhoff material, which is the simplest extension of a linear elastic material to the nonlinear regime, with mechanical properties aligned with those observed for adipose tissues (please refer to [Alkhouli, 2013]). The static solution of the nonlinear system of equations is computed with an iterative Newton-Raphson method, using a direct solver ([Schenk, 2004]), within the SOFA framework ([Faure, 2012]).

Aim of the study It has been observed that the context of the study is quite broad. The presented pipeline can indeed be employed to update simulation boundary conditions intra-operatively in different scenarios, provided that a pre-operative anatomical model is available, together with an intra-operative view of the tissue surface. Obtained results in terms of accuracy are specific to the surgical case considered in our validation (surgical manipulation of the kidney) and are influenced by the intrinsic accuracy of the finite element model chosen for the evaluation. It is worth highlighting that the presented pipeline works independently of the biomechanical model used in the evaluation phase. This suggests that higher accuracy levels might be obtained in a different application by selecting the most suitable biomechanical model for the context of interest. However, the accuracy levels obtained in this work are aligned with those required by model-guided intra-operative applications within a similar surgical context (i.e., laparoscopy).

Results The obtained experimental results show that the presented pipeline is suitable for intra-operative model update in a realistic environment. The analysis conducted on computation times accounts for the time required by each part of the pipeline, including the pre-processing step (point cloud acquisition, segmentation, registration). Missing comparison with other methods is due to a lack of previous works that address the problem of intra-operative update of boundary conditions. Existing works either rely on additional intra-operative sensors (see reference [Peterlik, 2014]) or focus on the estimation of boundary condition elasticity (references [Nikolaev, 2020] and [Peterlik, 2017]), thus preventing applicability to our scenario.

Reproducibility Being aware of MICCAI commitment to reproducible research, authors confirm that the presented pipeline is based on code freely available online, thus reproducible. It is in authors’ plans to extend the already existing and open BA-Net repository (https://gitlab.com/altairLab/banet) with the full intra-operative pipeline (including ZoomOut method). This action has not been done at paper submission time to comply with the double-blind revision process.

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Intra-operative Update of Boundary Conditions for Patient-specific Surgical Simulation