A Meta Registration Framework for Lesion Matching

Seshamani, Sharmishtaa; Rajan, Purnima; Kumar, Rajesh; Girgis, Hani Z.; Dassopoulos, T.; Mullin, Gerard E.; Hager, Gregory D.

doi:10.1007/978-3-642-04268-3_72

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Several newly published articles supported this consensus process, or the so-called meta-registration process, such as [72], [116]–[118]. Doshi et al recently combined ANTs and DRAMMS in a multi-atlas labeling framework, resulting in a top-performing method in a MICCAI segmentation challenge [72].…”

Section: Discussionmentioning

confidence: 99%

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Akbari

Bilello

et al. 2014

IEEE Trans. Med. Imaging

164

170

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Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations.

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Section: Discussionmentioning

confidence: 99%

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Akbari

Bilello

et al. 2014

IEEE Trans. Med. Imaging

164

170

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“…Such modifications could further aid the registration to focus on ROI, and could largely reduce the negative impacts from structures that are not of interest to the specific application. Finally, we could also explore methodologies to form a consensus mechanism, where multiple registration methods can complement each other toward more accurately capturing voxelwise volumetric changes [e.g., ].…”

Section: Discussionmentioning

confidence: 99%

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Weinstein

Conant

et al. 2014

Magnetic Resonance in Med

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Purpose To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. Methods Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation. Results DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. Conclusions The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

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“…Quantitative evaluations have shown that it can reduce the frame number down to less than 10% with almost no loss of information. In our future work, we plan to extend the semantically organized epitome for WCE image registration [13].…”

Section: Resultsmentioning

confidence: 99%

Epitomized Summarization of Wireless Capsule Endoscopic Videos for Efficient Visualization

Chu

Poh

et al. 2010

Lecture Notes in Computer Science

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A video recording of an examination by Wireless Capsule Endoscopy (WCE) may typically contain more than 55,000 video frames, which makes the manual visual screening by an experienced gastroenterologist a highly time-consuming task. In this paper, we propose a novel method of epitomized summarization of WCE videos for efficient visualization to a gastroenterologist. For each short sequence of a WCE video, an epitomized frame is generated. New constraints are introduced into the epitome formulation to achieve the necessary visual quality for manual examination, and an EM algorithm for learning the epitome is derived. First, the local context weights are introduced to generate the epitomized frame. The epitomized frame preserves the appearance of all the input patches from the frames of the short sequence. Furthermore, by introducing spatial distributions for semantic interpretation of image patches in our epitome formulation, we show that it also provides a framework to facilitate the semantic description of visual features to generate organized visual summarization of WCE video, where the patches in different positions correspond to different semantic information. Our experiments on real WCE videos show that, using epitomized summarization, the number of frames have to be examined by the gastroenterologist can be reduced to less than one-tenth of the original frames in the video.

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A Meta Registration Framework for Lesion Matching

Cited by 6 publications

References 11 publications

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Epitomized Summarization of Wireless Capsule Endoscopic Videos for Efficient Visualization

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