Performance Assessment for Brain MR Imaging Registration Methods

Lin, Jenshan; Fuentes, David; Chandler, Adrian; Prabhu, Sujit S.; Weinberg, Jeffrey S.; Baladandayuthapani, Veerabhadran; Hazle, John D.; Schellingerhout, Dawid

doi:10.3174/ajnr.a5122

Cited by 10 publications

(10 citation statements)

References 28 publications

(25 reference statements)

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“…For efficiency and to minimize both inter- and intra-user variability, several studies have explored segmentation to all relevant MRI sequences without registration across the sequences (33). Registration distortions between MRI series may limit this approach (34). Distortion could cause incorrect localization of the ROI, directing the radiomic analyses to the incorrect MRI-defined anatomy.…”

Section: Radiomics Methodologymentioning

confidence: 99%

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

et al. 2019

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Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.

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Section: Radiomics Methodologymentioning

confidence: 99%

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

et al. 2019

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“…The registration results can be visualized and evaluated with metrics such as TRE and EER. 25 We have also implemented registration of OAT images to a standard MRI brain atlas for further analysis. Finally, RegOA allows exporting registered results in a NIfTI format to other widely used imaging analysis toolbox, such as SPM 27 and AFNI, 26 for volume-based morphometry and voxelbased analysis.…”

Section: Framework Of Registrationmentioning

confidence: 99%

“…TRE is defined as the Euclidean distance between points (targets) that are used for registration algorithm. 25 EER, the fraction of Euclidean error remaining after registration, is defined as one TRE gap between two corresponding targets divided by their original distance before registration. 25 For phantom validation, the three inclusions were clearly visible in both OAT and MR images.…”

Section: Evaluation Of Registrationmentioning

confidence: 99%

“…25 EER, the fraction of Euclidean error remaining after registration, is defined as one TRE gap between two corresponding targets divided by their original distance before registration. 25 For phantom validation, the three inclusions were clearly visible in both OAT and MR images. The center of each inclusion served as ground-truth position of the registration targets.…”

Section: Evaluation Of Registrationmentioning

confidence: 99%

“…The accuracy of the algorithm was evaluated quantitatively by metrics, such as the target registration error (TRE) and the Euclidean effectiveness ratio (EER). 25 The robustness of the registration algorithm was tested by rotating the OAT images against fixed MRI images. RegOA also supports exporting the files to other well-established image analysis/registration platforms, such as AFNI 26 and SPM.…”

Section: Introductionmentioning

confidence: 99%

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Automated registration of magnetic resonance imaging and optoacoustic tomography data for experimental studies

et al. 2019

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Multimodal imaging combining optoacoustic tomography (OAT) with magnetic resonance imaging (MRI) enables spatiotemporal resolution complementarity, improves accurate quantification, and thus yields more insights into physiology and pathophysiology. However, only manual landmark based coregistration of OAT-MRI has been used so far. We developed a toolbox (RegOA), which frames an automated registration pipeline to align OAT with high-field MR images based on mutual information. We assessed the performance of the registration method using images acquired on one phantom with fiducial markers and in vivo/ex vivo data of mouse heads/brain. The accuracy and robustness of the registration are improved using a two-step registration method with preprocessing of OAT and MRI data. The major advantages of our approach are minimal user input and quantitative assessment of the registration error. The registration with MR and standard reference atlas enables regional information extraction, facilitating the accurate, objective, and rapid analysis of large groups of rodent OAT and MR images.

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Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net

Liu

Song

2022

Int. J. Mach. Learn. & Cyber.

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Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-of-interest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

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Performance Assessment for Brain MR Imaging Registration Methods

Abstract: BACKGROUND AND PURPOSE: Clinical brain MR imaging registration algorithms are often made available by commercial vendors without figures of merit. The purpose of this study was to suggest a rational performance comparison methodology for these products.

Cited by 10 publications

References 28 publications

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Automated registration of magnetic resonance imaging and optoacoustic tomography data for experimental studies

Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net

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