Objective Evaluation of Accuracy of Intra-Operative Neuroimage Registration

Garlapati, Revanth Reddy; Joldes, Grand Roman; Wittek, Adam; Lam, Jonathan; Weisenfeld, Neil; Hans, Arne; Warfield, Simon K.; Kikinis, Ron; Miller, Karol

doi:10.1007/978-1-4614-6351-1_9

Cited by 11 publications

(19 citation statements)

References 20 publications

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“…However, in general, both the percentile-EBHD metric and the percentile-RHD metric values deviate from the applied maximum deformation values (alignment errors), as their definitions do not include point-to-point correspondence (8, 21). Furthermore, it can be observed that the percentile-EBHD and percentile-RHD curves in the either axial or coronal planes are more sensitive to increase in maximum alignment error in a particular plane in case a larger component of the alignment error is concentrated in that plane (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…3). For more detailed information on round-trip consistency procedure, the reader is referred to our previous work (21). …”

Section: Methodsmentioning

confidence: 99%

“…The procedure for computing the directed distance between edge sets is different from the point-based directed Euclidean distance, as illustrated in Eqs. 2–4 (21). …”

Section: Methodsmentioning

confidence: 99%

“…The presence of such pairs of edges (outliers) leads to unusually large values of the EBHD, which are not representative of the actual registration accuracy. In order to eliminate such outliers, the registration accuracy analysis involves the computation of percentile-EBHD metric values in conjunction with visualisation of overlay of edges for 2D images containing potential outliers (21)(Fig. 4).…”

Section: Methodsmentioning

confidence: 99%

“…It has been demonstrated that point-sets forming edges or contours of segmented regions, may be used together with HD-based methods to estimate the misalignment of neuroimages (8, 20, 21). In this paper we analyse results obtained using two such methods: Edge-based Hausdorff Distance (EBHD) (21) and Robust Hausdorff Distance (RHD) (8) metrics.…”

Section: Introductionmentioning

confidence: 99%

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Towards measuring neuroimage misalignment

Garlapati¹,

Mostayed²,

Joldes³

et al. 2015

Computers in Biology and Medicine

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To enhance neuro-navigation, high quality pre-operative images must be registered onto intra-operative configuration of the brain. Therefore evaluation of the degree to which structures may remain misaligned after registration is critically important. We consider two Hausdorff Distance (HD)-based evaluation approaches: the edge-based HD (EBHD) metric and the Robust HD (RHD) metric as well as various commonly used intensity-based similarity metrics such as Mutual Information (MI), Normalized Mutual Information (NMI), Entropy Correlation Coefficient (ECC), Kullback-Leibler distance (KLD) and Correlation Ratio (CR). We conducted the evaluation by applying known deformations to simple sample images and real cases of brain shift. We conclude that the intensity-based similarity metrics such as MI, NMI, ECC, KLD and CR do not correlate well with actual alignment errors, and hence are not useful for assessing misalignment. On the contrary, the EBHD and the RHD metrics correlated well with actual alignment errors; however, they have been found to underestimate the actual misalignment. We also note that it is beneficial to present HD results as a percentile-HD curve rather than a single number such as the 95-percentile HD. Percentile-HD curves present full range of alignment errors and also facilitate the comparison of results obtained using different approaches. Furthermore, the qualities that should be possessed by an ideal evaluation metric were highlighted. Future studies could focus on developing such an evaluation metric.

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Section: Resultsmentioning

confidence: 99%

“…3). For more detailed information on round-trip consistency procedure, the reader is referred to our previous work (21). …”

Section: Methodsmentioning

confidence: 99%

“…The procedure for computing the directed distance between edge sets is different from the point-based directed Euclidean distance, as illustrated in Eqs. 2–4 (21). …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Towards measuring neuroimage misalignment

Garlapati¹,

Mostayed²,

Joldes³

et al. 2015

Computers in Biology and Medicine

Self Cite

View full text Add to dashboard Cite

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Biomechanical model for computing deformations for whole‐body image registration: A meshless approach

Mao

Miller

Joldes

et al. 2016

Numer Methods Biomed Eng

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Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2-D models and computing single organ deformations. In this study, 3-D comprehensive patient-specific non-linear biomechanical models implemented using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms are applied to predict a 3-D deformation field for whole-body image registration. Unlike a conventional approach which requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the Fuzzy C-Means (FCM) algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features.

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