Dr. Jon S. Heiselman Profile

Dr. Jon S. Heiselman

Postdoctoral Research Associate

SPIE Involvement:

Author

Publications (28)

Proceedings Article | 29 March 2024 Presentation + Paper

Optimal hyperparameter selection in deformable image registration using information criterion and band-limited modal reconstruction

Jon Heiselman, Morgan Ringel, Jayasree Chakraborty, William Jarnagin, Michael Miga

Proceedings Volume 12928, 129280V (2024) https://doi.org/10.1117/12.3008584

KEYWORDS: Image registration, Deformation, Data modeling, Performance modeling, Statistical modeling, Reconstruction algorithms, Mode shapes, Boundary conditions, Information theory, Spatial frequencies

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Image registration algorithms often depend on model parameters that can substantially impact registration accuracy. Current strategies for optimizing registration performance depend on retrospective assessment of accuracy measures such as target registration error (TRE) to identify the most appropriate model parameterization. However, this process of hyperparameter tuning may not produce results that adequately generalize to inter- and intra-dataset variabilities. In this work, we present an analysis framework based on the Akaike Information Criterion (AIC) that permits dynamic runtime adaptation of model parameters by maximizing the informativeness of the registration model with respect to the specific instance of available data constraints. We implement this parameter adaptation framework within a frequency band-limited boundary condition reconstruction approach to efficiently resolve modal harmonics of soft tissue deformation during image registration. We find that minimization of the AIC measure can be used as a surrogate for optimizing TRE when determining optimal model parameterization. Our registration approach automatically tunes model complexity to match informational constraints via an AIC-weighted ensemble model consisting of a collection of registration candidates computed in parallel. Within the context of image-to-physical registration on a registration challenge dataset, we show that our method achieves TRE comparable to other state-of-the-art methods of 4.86±1.07 mm, without the need for any hyperparameter tuning. When an exhaustive fine-tuning approach is applied, the band-limited reconstruction approach exhibits average TRE of 2.99±0.66 mm, which outperforms all other state-of-the-art registration methods contributed to the registration challenge. This technique is expected to improve registration accuracy and robustness by providing an information theoretically optimal strategy to adjust model parameters in a fully prospective manner when generalizing a registration algorithm to new data.

Proceedings Article | 29 March 2024 Presentation + Paper

Image guidance system for breast conserving surgery with integrated stereo camera monitoring and deformable correction

Morgan Ringel, Winona Richey, Jon Heiselman, Alexander Stabile, Ingrid Meszoely, Michael Miga

Proceedings Volume 12928, 129280C (2024) https://doi.org/10.1117/12.3007858

KEYWORDS: Surgery, Breast cancer, Deformation, Breast, 3D modeling, Stereoscopic cameras, Data modeling, Sensors, Imaging systems, Tumors, 3D image processing, Image registration

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SPIE Journal Paper | 4 March 2024

Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation

Bowen Xiang, Jon Heiselman, Winona Richey, Michael D’Angelica, Alice Wei, T. Peter Kingham, Frankangel Servin, Kyvia Pereira, Sunil Geevarghese, William Jarnagin, Michael Miga

JMI, Vol. 11, Issue 02, 025001, (March 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.025001

KEYWORDS: Liver, Natural surfaces, Deformation, Image registration, Surgery, Data acquisition, Data modeling, Rigid registration, Optical tracking, Image segmentation

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PurposeTo study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery.ApproachA Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from n=10 patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1) rigid registration method (RRM), (2) model-based nonrigid registration method (NRM) using surface data only, and (3) NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation.ResultsSurface data coverage collected by stylus and Conoprobe were 24.6%±6.4% and 19.6%±5.0%, respectively. The average difference between stylus data and Conoprobe data using NRM was −1.05 mm and using NRM-VC was −1.42 mm, indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66 mm, respectively.ConclusionThe first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.

SPIE Journal Paper | 8 January 2024 Open Access

The Image-to-Physical Liver Registration Sparse Data Challenge: comparison of state-of-the-art using a common dataset

Jon Heiselman, Jarrod Collins, Morgan Ringel, T. Peter Kingham, William Jarnagin, Michael Miga

JMI, Vol. 11, Issue 01, 015001, (January 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.1.015001

KEYWORDS: Image registration, Deformation, Liver, Biomechanics, Deep learning, Rigid registration, Data modeling, Detection and tracking algorithms, Natural surfaces, Boundary conditions

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PurposeComputational methods for image-to-physical registration during surgical guidance frequently rely on sparse point clouds obtained over a limited region of the organ surface. However, soft tissue deformations complicate the ability to accurately infer anatomical alignments from sparse descriptors of the organ surface. The Image-to-Physical Liver Registration Sparse Data Challenge introduced at SPIE Medical Imaging 2019 seeks to characterize the performance of sparse data registration methods on a common dataset to benchmark and identify effective tactics and limitations that will continue to inform the evolution of image-to-physical registration algorithms.ApproachThree rigid and five deformable registration methods were contributed to the challenge. The deformable approaches consisted of two deep learning and three biomechanical boundary condition reconstruction methods. These algorithms were compared on a common dataset of 112 registration scenarios derived from a tissue-mimicking phantom with 159 subsurface validation targets. Target registration errors (TRE) were evaluated under varying conditions of data extent, target location, and measurement noise. Jacobian determinants and strain magnitudes were compared to assess displacement field consistency.ResultsRigid registration algorithms produced significant differences in TRE ranging from 3.8±2.4 mm to 7.7±4.5 mm, depending on the choice of technique. Two biomechanical methods yielded TRE of 3.1±1.8 mm and 3.3±1.9 mm, which outperformed optimal rigid registration of targets. These methods demonstrated good performance under varying degrees of surface data coverage and across all anatomical segments of the liver. Deep learning methods exhibited TRE ranging from 4.3±3.3 mm to 7.6±5.3 mm but are likely to improve with continued development. TRE was weakly correlated among methods, with greatest agreement and field consistency observed among the biomechanical approaches.ConclusionsThe choice of registration algorithm significantly impacts registration accuracy and variability of deformation fields. Among current sparse data driven image-to-physical registration algorithms, biomechanical simulations that incorporate task-specific insight into boundary conditions seem to offer best performance.

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SPIE Journal Paper | 2 June 2023

Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration

Jon Heiselman, Brett Ecker, Liana Langdon-Embry, Eileen O’Reilly, Michael Miga, William R. Jarnagin, Richard K. Do, Natally Horvat, Alice Wei, Jayasree Chakraborty

JMI, Vol. 10, Issue 03, 036002, (June 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.3.036002

KEYWORDS: Tumors, Image registration, Pancreas, Image segmentation, Deformation, Radiography, Chemotherapy, Resection, Error analysis, Pancreatic cancer

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PurposePancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging.ApproachThirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study. Two image registration methods were applied to quantitatively assess longitudinal changes in tumor volume and tumor burden across the neoadjuvant treatment interval. Longitudinal registration errors of the pancreas were characterized, and registration-based treatment response measures were correlated to overall survival (OS) and recurrence-free survival (RFS) outcomes over 5-year follow-up. Corresponding biomarker assessments via manual tumor segmentation, the standardized response evaluation criteria in solid tumors (RECIST), and pathological examination of post-resection tissue samples were analyzed as clinical comparators.ResultsAverage target registration errors were 2.56 ± 2.45 mm for a biomechanical image registration algorithm and 4.15 ± 3.63 mm for a diffeomorphic intensity-based algorithm, corresponding to 1–2 times voxel resolution. Cox proportional hazards analysis showed that registration-derived changes in tumor burden were significant predictors of OS and RFS, while none of the alternative comparators, including manual tumor segmentation, RECIST, or pathological variables were associated with consequential hazard ratios. Additional ROC analysis at 1-, 2-, 3-, and 5-year follow-up revealed that registration-derived changes in tumor burden between pre- and post-treatment imaging were better long-term predictors for OS and RFS than the clinical comparators.ConclusionsVolumetric changes measured by longitudinal deformable image registration may yield imaging biomarkers to discriminate neoadjuvant treatment response in ill-defined tumors characteristic of PDAC. Registration-based biomarkers may help to overcome visual limits of radiographic evaluation to improve clinical outcome prediction and inform treatment selection.

Proceedings Article | 3 April 2023 Poster + Paper

Stereovision registration using a tracked checkerboard calibration object for a breast surgery image guidance system

Alexander Stabile, Morgan Ringel, Winona Richey, Jon Heiselman, Ingrid Meszoely, Michael Miga

Proceedings Volume 12466, 1246629 (2023) https://doi.org/10.1117/12.2655703

KEYWORDS: Calibration, Breast, Stereoscopic cameras, Optical tracking, Deformation, Cameras, Surgery, Image registration, Imaging systems, Tissues

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Proceedings Article | 3 April 2023 Presentation + Paper

Digital twin forecasting of microwave ablation via fat quantification image-to-grid computational methods

Frankangel Servin, Jarrod Collins, Jon Heiselman, Katherine Frederick-Dyer, Virginia Planz, Sunil Geevarghese, Daniel Brown, Michael Miga

Proceedings Volume 12466, 1246619 (2023) https://doi.org/10.1117/12.2655257

KEYWORDS: Ablation, Liver, Diseases and disorders, Materials properties, Data modeling, Thermal modeling, Microwave radiation, Tumor growth modeling, 3D modeling, Tumors

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Proceedings Article | 3 April 2023 Presentation + Paper

Comparison study of sparse data-driven soft tissue registration: preliminary results from the image-to-physical liver registration sparse data challenge

Jon Heiselman, Jarrod Collins, Morgan Ringel, William Jarnagin, Michael Miga

Proceedings Volume 12466, 124660M (2023) https://doi.org/10.1117/12.2655468

KEYWORDS: Deformation, Image registration, Liver, Rigid registration, Biomechanics, Data modeling, Reconstruction algorithms, Deep learning, Natural surfaces, Detection and tracking algorithms

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Proceedings Article | 3 April 2023 Presentation + Paper

Comparison study of intraoperative surface acquisition methods for surgical navigation

Bowen Xiang, Jon Heiselman, Winona Richey, William Jarnagin, Michael Miga

Proceedings Volume 12466, 124660N (2023) https://doi.org/10.1117/12.2655971

KEYWORDS: Liver, Image registration, Deformation, Surgery, Data modeling, Rigid registration, 3D modeling, Ultrasonography, Natural surfaces, Calibration

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In select patients when transplant is not possible, liver surgery is a preferred treatment for liver cancer and is performed with curative intent. Currently, only about 20% of patients are eligible for resection due to the complexity of the procedure. Image-Guided Liver Surgery (IGLS) that utilizes preoperative Computed Tomographic imaging (CT) data is not yet standard of care, and one of the confounding factors toward its realization in the liver is the presence of soft-tissue deformations that compromise the fidelity of these systems. IGLS systems involve intraoperative data collection to achieve image-to-physical alignments, and realizations to date have previously used an optically tracked stylus that requires physical contact with the liver. One source of error in this process involves contact pressure that may cause inaccuracies during the acquisition of liver shape data. In this study, we use a non-contact Conoprobe digitization method for comparison against stylus-based acquisition. We developed a novel Conoprobe device attachment and sterilization process to enable prospective data acquisition in the operating room. The goal of this work is to study the difference between rigid registration and non-rigid registration with respect to two forms of digitization (contact and non-contact) in vivo. For this preliminary work, data from one patient undergoing liver resection was analyzed from our novel prototype under an IRB approved study at Memorial Sloan Kettering Cancer Center. The organ surface coverage of the two digitization methods was compared. Rigid and model-based non-rigid registration were performed and evaluated for a patient undergoing liver surgery. Segmented contours of the ultrasound-identified targets were compared to their registered preoperative counterparts for accuracy validation. The findings indicate that surface coverage of the Conoprobe is less than that of stylus, suggesting that non-contact accuracy benefits may be obscured by inferior data coverage. However, more investigation is needed to potentially increase Conoprobe surface data extent during acquisition.

Proceedings Article | 3 April 2023 Presentation + Paper

Accounting for brain shift during image-guided tumor resection surgeries: an intraoperative feasibility study

Michael Miga, Ma Luo, Jaime Tierney, Winona Richey, Jon Heiselman, Reid Thompson

Proceedings Volume 12466, 1246614 (2023) https://doi.org/10.1117/12.2655531

KEYWORDS: Surgery, Resection, Brain, Deformation, Tumors, Prototyping, Neuroimaging, Imaging systems, Image-guided intervention, Model-based design

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Image-guided neuro navigation systems are still reliant on the rigid alignment of preoperative tomographic imaging information (usually magnetic resonance imaging data) to the intraoperative patient anatomy. It is well understood that soft tissue deformations during surgery can compromise that alignment leading to significant discrepancies between imaged neuroanatomy and its physical space counterpart. While intraoperative MRI/CT are available, the encumbrance, cost, and workflow have inhibited the widespread adoption as a standard of care. As a result, computational imaging efforts to adjust for deformations have been under investigation. The goal of this work was to perform a feasibility study to evaluate a model-based strategy to correct for deformations and evaluate in real-time. For this study n=8 subjects were enrolled in an IRB approved study at Vanderbilt University Medical Center. Model-based deformation correction was performed and evaluated for six of the eight surgeries with a total of seven evaluations performed (in one case, both the attending and resident evaluated the correction separately). With respect to evaluation, at the end of each surgery, the model-deformed images were displayed next to the gold standard preoperative images in the operating room. A stylus was used by the surgeon to interrogate the surgical field and evaluate the alignments between the model-based corrected guidance system and standard-of-care conventional IGS system. For all cases with substantial shift, which was six of the seven evaluations, surgeons preferred the model-based approach in terms of image alignment to patient anatomy. For the case with minimal shift, the surgeon found no difference between the two systems. For six of the seven evaluations, surgeons had an overall preference for the model-based approach. In conclusion, we demonstrated initial feasibility of using a model-based deformation correction scheme during brain tumor resection surgeries. Additionally, based on the surgeon consensus of improved image alignment to intraoperative anatomy, this study demonstrates the potential benefit of our approach in terms of evaluating resection boundaries intraoperatively.

Proceedings Article | 3 April 2023 Poster + Paper

Modeling retraction for breast conserving surgery guidance

Amairanny Espinosa, Morgan Ringel, Jon Heiselman, Kyvia Pereira, Frankangel Servin, Winona Richey, Ingrid Meszoely, Michael Miga

Proceedings Volume 12466, 1246628 (2023) https://doi.org/10.1117/12.2655662

KEYWORDS: Deformation, Breast, Modeling, Surgery, Computed tomography, Tissues, Silicon, Tumors, Biomechanics, Inverse problems

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SPIE Journal Paper | 14 November 2022

Supine magnetic resonance image registration for breast surgery: insights on material mechanics

Morgan Ringel, Winona Richey, Jon Heiselman, Ma Luo, Ingrid M. Meszoely, Michael Miga

JMI, Vol. 9, Issue 06, 065001, (November 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.6.065001

KEYWORDS: Image registration, Breast, Tissues, Data modeling, Magnetic resonance imaging, Surgery, Model-based design, Mechanics, Magnetism, Image segmentation

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PurposeBreast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, is being explored since it more closely represents the surgical presentation compared to conventional diagnostic imaging positions. Despite this preoperative imaging position, deformation is still present between the supine imaging and surgical state. As a result, a fast and accurate image-to-physical registration approach is needed to realize image-guided breast surgery.ApproachIn this study, three registration methods were investigated on healthy volunteers’ breasts (n = 11) with the supine arm-down position simulating preoperative imaging and supine arm-up position simulating intraoperative presentation. The registration methods included (1) point-based rigid registration using synthetic fiducials, (2) nonrigid biomechanical model-based registration using sparse data, and (3) a data-dense three-dimensional diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. Additionally, deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field to better understand breast material mechanics.ResultsThe average target registration errors (TRE) were 10.4 ± 2.3, 6.4 ± 1.5, and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7, 2.5 ± 1.1, and 3.1 ± 1.1 mm for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. The mechanics-based deformation metrics revealed an overall anisotropic tissue behavior and a statistically significant difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy.ConclusionsOverall, registration accuracy significantly improved with increasingly flexible and data-dense registration methods. Analysis of these outcomes may inform the future development of image guidance systems for lumpectomy procedures.

Proceedings Article | 4 April 2022 Presentation + Paper

Digital application to display brain shift simulation in tumor resection procedures

Kush Hari, Rohan Vijayan, Ma Luo, Jaime Tierney, Jon Heiselman, Lola Chambless, Reid Thompson, Michael Miga

Proceedings Volume 12034, 120341J (2022) https://doi.org/10.1117/12.2611479

KEYWORDS: Brain, Surgery, 3D displays, 3D modeling, Prototyping, Mixed reality, Head, Tumors, Neuroimaging, Data modeling

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Proceedings Article | 4 April 2022 Presentation + Paper

Breast image registration for surgery: insights on material mechanics modeling

Morgan Ringel, Winona Richey, Jon Heiselman, Ma Luo, Ingrid Meszoely, Michael Miga

Proceedings Volume 12034, 1203411 (2022) https://doi.org/10.1117/12.2611787

KEYWORDS: Image registration, Breast, Model-based design, Magnetic resonance imaging, 3D modeling, Data modeling, Surgery, Rigid registration

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Proceedings Article | 4 April 2022 Presentation + Paper

Tumor deformation correction for an image guidance system in breast conserving surgery

Winona Richey, Jon Heiselman, Morgan Ringel, Ingrid Meszoely, Michael Miga

Proceedings Volume 12034, 120340K (2022) https://doi.org/10.1117/12.2611570

KEYWORDS: Tumors, Breast, Surgery, Rigid registration, Magnetic resonance imaging, Finite element methods, Breast cancer, Breast imaging, Computational modeling

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Proceedings Article | 4 April 2022 Presentation + Paper

Registration uncertainty in deforming organs: a novel approach for ensuring navigational confidence during image-guided procedures

Jon Heiselman, William Jarnagin, Michael Miga

Proceedings Volume 12034, 120340Z (2022) https://doi.org/10.1117/12.2612938

KEYWORDS: Image registration, Error analysis, Data modeling, Rigid registration, Navigation systems, Tissues, Surgery, Natural surfaces, Binary data, Image-guided intervention

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Proceedings Article | 15 February 2021 Presentation + Paper

Improving accuracy of image-to-physical laparoscopic liver registration via reconstruction of intrahepatic pressure changes from abdominal insufflation

Jon Heiselman, Winona Richey, Shannon Taylor, Michael Miga

Proceedings Volume 11598, 115980W (2021) https://doi.org/10.1117/12.2582155

KEYWORDS: Image registration, Liver, Laparoscopy, Tissues, Rigid registration, Surgery, Hemodynamics

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Proceedings Article | 16 March 2020 Presentation + Paper

Image data-driven thermal dose prediction for microwave ablation therapy

Alice Ding, Jon Heiselman, Michael Miga

Proceedings Volume 11315, 113151G (2020) https://doi.org/10.1117/12.2550550

KEYWORDS: Tumors, Liver, Image registration, Computed tomography, Rigid registration, Microwave radiation, Tissues, Optical tracking, Image segmentation

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Proceedings Article | 16 March 2020 Presentation + Paper

The image-to-physical liver registration sparse data challenge: characterizing inverse biomechanical model resolution

Jon Heiselman, Michael Miga

Proceedings Volume 11315, 113151F (2020) https://doi.org/10.1117/12.2550535

KEYWORDS: Liver, Image registration, Data modeling, Surgery, Natural surfaces, Data acquisition, Reconstruction algorithms, Algorithm development

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Proceedings Article | 16 March 2020 Presentation + Paper

Textual fiducial detection in breast conserving surgery for a near-real time image guidance system

Winona Richey, Jon Heiselman, Ma Luo, Ingrid Meszoely, Michael Miga

Proceedings Volume 11315, 113151L (2020) https://doi.org/10.1117/12.2550662

KEYWORDS: Breast, Stereoscopic cameras, Surgery, Tumors, Optical tracking, Imaging systems, Optical character recognition, Breast cancer, 3D modeling, 3D image processing

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SPIE Journal Paper | 20 May 2019

Multiphysics modeling toward enhanced guidance in hepatic microwave ablation: a preliminary framework

Jarrod Collins, Jon Heiselman, Logan Clements, Daniel Brown, Michael Miga

JMI, Vol. 6, Issue 02, 025007, (May 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.025007

KEYWORDS: Image registration, Antennas, Rigid registration, Microwave radiation, Data modeling, Tissues, Natural surfaces, Liver, Thermal modeling, Model-based design

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Proceedings Article | 8 March 2019 Paper

Quantitative imaging analysis to guide biopsy for molecular biomarkers

Derek Doss, Jon Heiselman, Ma Luo, Logan Clements, Michael Miga, Daniel Brown, Filip Banovac

Proceedings Volume 10951, 109512V (2019) https://doi.org/10.1117/12.2513588

KEYWORDS: Liver, Tumors, Image registration, Biopsy, Image segmentation, Liver cancer, Biological research, Rigid registration, Natural surfaces, Cancer

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Although resection and transplantation are primary curative methods of treatment for hepatocellular carcinoma, many patients are not candidates. In these cases, other treatment methods such as selective internal radiation therapy, chemotherapy, or external beam radiation are used. While these treatments are effective, patient-specific customization of treatment could be beneficial. Recent advances in personalized medicine are making this possible, but often there are multiple phenotypes within a proliferating tumor. While not standard, one could envision a serial longitudinal biopsy approach with more phenotypically-targeted therapeutics if one could detect responding and non-responding regions of tumor over time. This work proposes a method to determine active regions of the tumor that differentially respond to treatment to better guide biopsy for longitudinal personalization of treatment. While PET may serve this purpose, it is not easily used for real-time image guidance, is not effective for many types of tumors, and can be confounded by inflammatory responses. In this work, ten total patients with imaging sequences from before and after treatment were retrospectively obtained. Five of these were selected for analysis based on the total liver volume change. A two-phase alignment process comprised of an intensity-based rigid registration followed by a nonrigid refining process driven by bulk deformation of the organ surface was performed. To assess the accuracy of the registration, two metrics were used for preliminary results. The mean closest point surface distance was used to quantify how well the surfaces of the registered livers match and was found to be 2.65±3.54mm. Anatomical features visible in pre- and post-treatment images were also identified. After registration, the mean Euclidean distance between features was found to be 5.22±4.06mm. To assess potential areas of tumor change, the registered tumor pre- and post-treatment were overlaid.

Proceedings Article | 8 March 2019 Presentation + Paper

The image-to-physical liver registration sparse data challenge

E. Lee Brewer, Logan Clements, Jarrod Collins, Derek Doss, Jon Heiselman, Michael Miga, Chris Pavas, Edward Wisdom

Proceedings Volume 10951, 109511F (2019) https://doi.org/10.1117/12.2513952

KEYWORDS: Liver, Image registration, Surgery, Computed tomography, Finite element methods, Computational modeling

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Proceedings Article | 12 March 2018 Presentation + Paper

Technical note: nonrigid registration for laparoscopic liver surgery using sparse intraoperative data

Jon Heiselman, Jarrod Collins, Logan Clements, Jared Weis, Amber Simpson, Sunil Geevarghese, T. Peter Kingham, William Jarnagin, Michael Miga

Proceedings Volume 10576, 105760D (2018) https://doi.org/10.1117/12.2295026

KEYWORDS: Laparoscopy, Liver, Natural surfaces, Surgery, Image registration, Data modeling, Image-guided intervention, Tissues, Rigid registration

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SPIE Journal Paper | 14 December 2017

Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery

Jon Heiselman, Logan Clements, Jarrod Collins, Jared Weis, Amber Simpson, Sunil Geevarghese, T. Peter Kingham, William Jarnagin, Michael Miga

JMI, Vol. 5, Issue 02, 021203, (December 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.021203

KEYWORDS: Laparoscopy, Liver, Natural surfaces, Data modeling, Surgery, Tissues, Image-guided intervention, Rigid registration, Error analysis, Data conversion

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Proceedings Article | 3 March 2017 Paper

On the nature of data collection for soft-tissue image-to-physical organ registration: a noise characterization study

Jarrod Collins, Jon Heiselman, Jared Weis, Logan Clements, Amber Simpson, William Jarnagin, Michael Miga

Proceedings Volume 10135, 101351Y (2017) https://doi.org/10.1117/12.2255844

KEYWORDS: Image registration, Liver, Image-guided intervention, Surgery, Tissues, Rigid registration, Data acquisition, Data analysis, Data modeling, Data centers, Computed tomography

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Proceedings Article | 3 March 2017 Paper

Using an Android application to assess registration strategies in open hepatic procedures: a planning and simulation tool

Derek Doss, Jon Heiselman, Jarrod Collins, Jared Weis, Logan Clements, Sunil Geevarghese, Michael Miga

Proceedings Volume 10135, 101351Z (2017) https://doi.org/10.1117/12.2255976

KEYWORDS: Image registration, Surgery, Liver, Image-guided intervention, Natural surfaces, Soft tissue optics, Tissue optics, Optical tracking, Tissues, Image restoration, Tablets, Rigid registration, Data modeling, Gold, 3D modeling

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Proceedings Article | 3 March 2017 Paper

Emulation of the laparoscopic environment for image-guided liver surgery via an abdominal phantom system with anatomical ligamenture

Jon Heiselman, Jarrod Collins, Logan Clements, Jared Weis, Amber Simpson, Sunil Geevarghese, William Jarnagin, Michael Miga

Proceedings Volume 10135, 101352W (2017) https://doi.org/10.1117/12.2255842

KEYWORDS: Laparoscopy, Image registration, Liver, Surgery, Image-guided intervention, Abdomen, Holography, Data acquisition, Rigid registration, Computed tomography

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