Multi-atlas registration-based segmentation is a popular technique in the medical imaging community, used to transform anatomical and functional information from a set of atlases onto a new patient that lacks this information. The accuracy of the projected information on the target image is dependent on the quality of the registrations between the atlas images and the target image. Recently, we have developed a technique called AQUIRC that aims at estimating the error of a non-rigid registration at the local level and was shown to correlate to error in a simulated case. Herein, we extend upon this work by applying AQUIRC to atlas selection at the local level across multiple structures in cases in which non-rigid registration is difficult. AQUIRC is applied to 6 structures, the brainstem, optic chiasm, left and right optic nerves, and the left and right eyes. We compare the results of AQUIRC to that of popular techniques, including Majority Vote, STAPLE, Non-Local STAPLE, and Locally-Weighted Vote. We show that AQUIRC can be used as a method to combine multiple segmentations and increase the accuracy of the projected information on a target image, and is comparable to cutting edge methods in the multi-atlas segmentation field.
In deep brain stimulation surgeries, stimulating electrodes are placed at specific targets in the deep brain to treat
neurological disorders. Reaching these targets safely requires avoiding critical structures in the brain. Meticulous
planning is required to find a safe path from the cortical surface to the intended target. Choosing a trajectory
automatically is difficult because there is little consensus among neurosurgeons on what is optimal. Our goals are to
design a path planning system that is able to learn the preferences of individual surgeons and, eventually, to standardize
the surgical approach using this learned information. In this work, we take the first step towards these goals, which is to
develop a trajectory planning approach that is able to effectively mimic individual surgeons and is designed such that
parameters, which potentially can be automatically learned, are used to describe an individual surgeon's preferences. To
validate the approach, two neurosurgeons were asked to choose between their manual and a computed trajectory, blinded
to their identity. The results of this experiment showed that the neurosurgeons preferred the computed trajectory over
their own in 10 out of 40 cases. The computed trajectory was judged to be equivalent to the manual one or otherwise
acceptable in 27 of the remaining cases. These results demonstrate the potential clinical utility of computer-assisted path
planning.
Many rigid and affine registration methods rely on optimizing an intensity-based similarity criterion between images.
Once registered, however, it is difficult to assess the quality of the registration based solely on the value of the similarity
measure. Past work in quantitative error analysis relies on the availability of fiducial markers. Little work has been done
on developing techniques that would permit assessing the registration quality between images that do not contain fiducial
markers without manual intervention. In this paper, we present an automatic technique that permits to do so. We apply
our method to estimate the registration quality of 10 MR and CT pairs and 10 MR and MR-contrast pairs. We show that
our technique is capable of detecting cases with registration error larger than 2° around one axis. We also show that our
method is better able to identify error in MR to CT registrations than popular similarity measures. Work is under way to
better determine the sensitivity of the technique.
A number of groups have reported on the occurrence of intra-operative brain shift during deep brain stimulation (DBS)
surgery. This has a number of implications for the procedure including an increased chance of intra-cranial bleeding and
complications due to the need for more exploratory electrodes to account for the brain shift. It has been reported that the
amount of pneumocephalus or air invasion into the cranial cavity due to the opening of the dura correlates with intraoperative
brain shift. Therefore, pre-operatively predicting the amount of pneumocephalus expected during surgery is of
interest toward accounting for brain shift. In this study, we used 64 DBS patients who received bilateral electrode
implantations and had a post-operative CT scan acquired immediately after surgery (CT-PI). For each patient, the
volumes of the pneumocephalus, left ventricle, right ventricle, third ventricle, white matter, grey matter, and cerebral
spinal fluid were calculated. The pneumocephalus was calculated from the CT-PI utilizing a region growing technique
that was initialized with an atlas-based image registration method. A multi-atlas-based image segmentation method was
used to segment out the ventricles of each patient. The Statistical Parametric Mapping (SPM) software package was
utilized to calculate the volumes of the cerebral spinal fluid (CSF), white matter and grey matter. The volume of
individual structures had a moderate correlation with pneumocephalus. Utilizing a multi-linear regression between the
volume of the pneumocephalus and the statistically relevant individual structures a Pearson's coefficient of r = 0.4123 (p
= 0.0103) was found. This study shows preliminary results that could be used to develop a method to predict the amount
of pneumocephalus ahead of the surgery.
Two popular segmentation methods used today are atlas based and graph cut based segmentation techniques. The atlas
based method deforms a manually segmented image onto a target image, resulting in an automatic segmentation. The
graph cut segmentation method utilizes the graph cut paradigm by treating image segmentation as a max-flow problem.
A specialized form of this algorithm was developed by Lecoeur et al [1], called the spectral graph cut algorithm. The
goal of this paper is to combine both of these methods, creating a more stable atlas based segmentation algorithm that is
less sensitive to the initial manual segmentation. The registration algorithm is used to automate and initialize the spectral
graph cut algorithm as well as add needed spatial information, while the spectral graph cut algorithm is used to increase
the robustness of the atlas method. To calculate the sensitivity of the algorithms, the initial manual segmentation of the
atlas was both dilated and eroded 2 mm and the segmentation results were calculated. Results show that the atlas based
segmentation segments the thalamus well with an average Dice Similarity Coefficient (DSC) of 0.87. The spectral graph
cut method shows similar results with an average DSC measure of 0.88, with no statistical difference between the two
methods. The atlas based method's DSC value, however, was reduced to 0.76 and 0.67 when dilated and eroded
respectively, while the combined method retained a DSC value of 0.81 and 0.74, with a statistical difference found
between the two methods.
Intensity overlap often occurs in medical images, making it difficult to identify different anatomical structures using
intensity alone. Research studies have shown that texture is an important component in quantifying the visual
appearance of anatomical structures, and is therefore valuable in the analysis, interpretation, and retrieval of lung
nodules.
The goal of our research study is to present a comparison between the different texture models: Gabor filters, Markov
Random Field (MRF), and global & local co-occurrence. For comparison purposes we utilized Manhattan, Euclidean,
and Chebyshev distances for one-dimensional feature vectors (global co-occurrence) while for two-dimensional feature
comparison (local co-occurrence, Gabor filters, and MRF) we utilized the similarity measures Chi-Square and Jeffrey-
Divergence. Local co-occurrence contains many different variable aspects in its design that can considerably change the
success of its results. A thorough examination of local co-occurrence's variables is discussed.
All of the discussed texture models are presented in the context of our previous Content-Based Image Retrieval (CBIR)
System [1]. BRISC utilizes the Lung Image Database Consortium (LIDC) database. We have found that Gabor and
MRF texture descriptors produce the best retrieval results regardless of the nodule size, number of retrieved items or
similarity metric with an average precision of 88%. Global co-occurrence performed the worse at 44% precision yet
when co-occurrence was performed locally (local co-occurrence) the precision results improved to 64%. A combination
of all the features worked the best with 91% precision.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.