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CiiS Lab
Johns Hopkins University
112 Hackerman Hall
3400 N. Charles Street
Baltimore, MD 21218
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Lab Director
Russell Taylor
127 Hackerman Hall
rht@jhu.edu
Last updated: May 9, 2014; 22:46 EDT
Statistical Shape Models are the primary topic of this project, however several sub-topics include surface and volumetric deformable registration, volumetric segmentation, feature extraction, and surface extrapolation. The goal is to design and implement a method for extrapolating missing anatomical craniofacial skeletal structure with the use of a statistical shape model of the human cranium.
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We intend to apply the algorithms developed for this project to the field of craniofacial surgery. The procedure of interest is craniofacial transplantation, which is the process of transplanting a donor’s craniofacial soft tissue, and possibly bone structure, onto a patient that has been subject to some severe craniofacial deformation. The surgery aims to restore lost functionality to the patient, such as the ability to smell, speak, or eat solid food [6]. By allowing the patient to participate in society as a “normal” individual, the surgery may help alleviate psychosocial traumas developed by the patient upon their disfiguration [6]. Figure 2 shows preoperative and postoperative views of a transplant recipient. Once a potential donor has been identified, the decision to perform surgery must be made within a very short time frame (24-36 hours)[7]. Amongst other factors, the skeletal structure of the patient and donor is compared for compatibility via cephalometric measurements [7]. We propose an attempt to estimate the skeletal structure of the patient’s face prior to disfiguration/injury. Once this estimated structure is completed for the patient, it may be matched quickly for compatibility with the donor’s skeletal structure and provide additional insight related to the patient-donor compatibility. If this skeletal comparison is accurate and useful, the surgeons may reprioritize their available time with additional refinement of the surgical plan or evaluation of other compatibility issues. It should be noted that the estimation of the patient’s ideal skeletal structure is an attempt to maximize the aesthetic quality of the surgical result, and makes no guarantee regarding the postoperative biomechanics of the patient. It is plausible that higher aesthetic quality will imply “good” biomechanics, but this will need to be the topic of further study. By leveraging techniques developed in the forensic facial reconstruction community [4], combined with the estimate of the patient’s true craniofacial skeletal structure, it may also be possible to create a model reflecting patient’s true physical appearance.
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This project consists of the following high-level technical components:
Once an atlas of skeletal surfaces has been created, the patient's skeletal surface will be deformably registered to the atlas, yielding an estimate of the patient's surface without abnormal pathology. The region of the patient's skeletal surface containing the abnormalities will be replaced with the corresponding regions from the atlas estimate. Additional processing will be required to remove any “jagged edges,” or discontinuities, as a result of the replacement. Figure 1 depicts a high level overview of the proposed reconstruction algorithm. Further details for each component are described in this section.
For this project, manual segmentation of the skeletal structure of the cranium will be performed. Open source, freely available, tools such as MITK, ITK Snap, and 3D Slicer may be used for this purpose [14][15][12]. This will most likely result in a more accurate segmentation compared to an automated method, and also avoid the “black art” of segmentation parameter tuning.
The general method we propose for atlas creation consists of the following steps and is a derivation of that found in Chintalapani, et al. [2]:
This approach differs most noticeably from the approach in [2], in that we will utilize only surface based mesh structures and operate on shape information only. We anticipate adequate results, even without the use of image intensity values, since the bone density of the skull is not of immediate importance.
We are currently evaluating two distinct methods for completing step 2 (creating MT, M1, M2, …, MN) in the general atlas creation creation method. The first method is summarized in the following steps:
The second method under consideration is summarized as follows:
We plan to utilize Matlab, ITK, 3D Slicer, and MeshLab for mesh creation and modification operations [11][9][12][3].
After initial development of an atlas creation pipeline and structural extrapolation technique, we hope to integrate an iterative “bootstrapping” mechanism into the atlas creation process. We plan on following a similar approach as [2], with the exception of using only surface data.
In order to simulate a patient with some missing, or deformed, skeletal anatomy we plan on using MeshLab [3] to perform a straightforward cutting, or cropping, of the skeletal surface. We intend to perform an initial estimate of the patient’s surface using the “Statistical Atlas-Based Extrapolation of CT Data” method described in [1]. We refer to the existing skeletal structure of the patient, with deformities or missing structure, as the “known” structure, and any estimates using the atlas as the “estimated” structure. Denote the known surface mesh of the patient as M; it may be partitioned into two regions as shown in Equation 1, with one region representing the deformed region of the patient (MTransplant) and the other representing the unchanged region of the patient (MKeep). We can then perform a deformable surface registration of M to the previously created atlas; this yields an estimate of the “normal” patient (M') in terms of the mean shape (v0) and the modes (vi for i = 1, …, NM) as shown in Equation 2. We intend to use the method developed as part of CIS I Programming Assignment 5 to perform this registration [8]. An analogous partitioning of M' may then be created to obtain the estimate of the patient’s missing structure, shown in Equation 3. Replacing MTransplant with M'Transplant in M yields, an estimate of the patient's true skeletal structure, M∗, as shown in Equation 4.
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In most cases M∗ is an insufficient estimate, due to a discontinuity on the edge joining MKeep with M'Transplant [1], therefore some “smoothing” process is desirable. We hope to build off of existing reconstructive techniques in forensic anthropology [13]. Most of these techniques utilize thin plate spline (TPS) interpolation/extrapolation. Figure 3 depicts a “toy” one-dimensional example we have created for creating a smooth surface from two discontinuous inputs. We used a cubic-spline for interpolation in this example, which is the one-dimensional analog to the TPS. Matlab or ITK may be utilized for TPS implementations [11][9]. The smooth extrapolation was not implemented this semester due to insufficient time. Given sufficient time, we would also like to simulate a more realistic trauma to the patient (as shown in the middle of figure 1), and evaluate it’s registration to the atlas, and subsequent surface reconstruction.
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Additionally, we plan on developing a similarity metric between an estimated patient skeletal structure and a potential donor's skeletal structure. This could initially be completed via a rigid registration and a computation of the Euclidian distance from the donor's surface to the estimated patient's surface within the transplant region of interest.
A joint effort between Johns Hopkins Hospital and Johns Hopkins Applied Physics Laboratory (APL) is currently underway to utilize preoperative CT data of the patient and donor, along with intraoperative processing, to obtain superior surgical outcomes [5]. We would like to extend this reconstruction method to assist in the craniofacial surgical planning phase of the APL project. The estimated skeletal structure may be able to enhance the selection of cutting planes for a transplant with minimal skeletal discontinuities.
We are also interested in developing a future system architecture that would incorporate a shared atlas amongst several participating hospitals, and an automated ability for obtaining a patient's skeletal reconstruction. Opposed to our manual segmentation of the skeletal regions of each cranium, an automated method should be employed or developed. Recent segmentation techniques, such Krach's “Sheetless Segmentation,” should be able to provided sufficient results [10]. An automated method of re-bootstrapping the atlas will need to be designed as well, so that the atlas may represent a larger segment of the population. If patient-donor compatibility is a concern, then a system of matching potential donors to patients, as the donors become available, would be another component. This would be relevant for automatically notifying the patient's surgeon when an ideal match for the patient becomes available in a different geographic locale; this could allow the donor to be immediately transported to the patient's institution before it becomes inviable for transplant. This system architecture would depend on consistent surgical planning, execution, and postoperative procedures across all participating institutions.
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Summary: As of May 9, 2014, we have met our minimum and expected deliverables. External Link
