eEdE-73 Introduction to Segmentation, Registration and Volume Analysis for Imaging Genomics Ginu A. Thomas, MD; Ahmed Elakkad, MD; Mohamed Elbanan, MD; Pascal O. Zinn, MD; Rivka R. Colen, MD. Section of Neuroradiology Department of Diagnostic Radiology MD Anderson Cancer Center Houston, Texas.

Disclosures No Disclosures

Questions Zinn et al. in 2011 uncovered which Gene/miRNA that is associated with high FLAIR signal in GBM? FHL2 PDGF POSTN TNFSF4 2. Diehn et al. discovered the association between high contrast enhancement to necrosis ratio and which of the following? CAV1 EGFR PGK VEGF

Purpose Image processing is essential for extracting data for any type of research study dealing with quantitative imaging variables. Before any type of volumetric analysis, the volumes in question should be well defined and presented as data types that are easy to deal with. In this exhibit we look at the methodology for image registration, segmentation and model building that we used for our study imaging genomic mapping in Glioblastoma (GBM).

Background RSNA’s Quantitative Imaging Biomarkers Alliance (QIBA) definition of Quantitative imaging: It is the extraction of quantifiable features from medical images for the assessment of normal or the severity, degree of change, or status of a disease, injury, or chronic condition relative to normal.”

Background Across different modalities, MRI gives the largest amount of quantifiable data. Radiologists usually perform some sort of quantitative imaging in their daily clinical practice, from measuring and annotating abnormalities to determining tissue perfusion or diffusion and tracer uptake.

Approach/Methods We used 3D Slicer software for image visualization, analysis, manipulation, segmentation, obtaining tumor volumetry and 3D visualization. 3D Slicer is a product of continuous international collaboration led by the Surgical Planning Lab at Brigham & Women’s Hospital.

Approach/Methods The platform has about 125 different modules and provides functionality for segmentation, registration and three dimensional visualization of multimodal image data, as well as advanced image analysis algorithms for diffusion tensor imaging, functional magnetic resonance imaging and image guided therapy.

Approach/Methods Cont’d We used two volumes for our project: FLAIR series for segmentation of the edema/invasion (Fast Spin Echo (FSE)  T2 or PD if FLAIR is not available). Post-contrast T1 weighted imaging (T1WI) series for segmentation of the tumor enhancing portion and the necrosis. We used Spoiled Gradient Echo Recaller (SPGR) or PWI series if T1 Post contrast wasn’t available). 

Approach/Methods Cont’d T1 POST with enhancing portion depicted using yellow label and necrosis using red label in this Left Temporal GBM

Approach/Methods Cont’d FLAIR Image registered over the T1POST to get accurate quantification of edema/Invasion (Blue label)

Approach/Methods Cont’d Each sequence was loaded in Slicer as a volume. The 'Parse Directory' function was used to parse the series and show all the series available in the data.

Approach/Methods Cont’d Image visualization: We selected all the desired series separately to be opened in the Volume module. The Volume module loads saves and adjusts the display parameters of volume data. A volume can be set for raw images of a certain sequence or label map and various options are available to adjust the way a volume is displayed.

Approach/Methods Cont’d It provides information about the volumes displayed such as image dimensions, image spacing, image origin, scan order, number of scalars, type of scalar and window/level presets. Various options are available to adjust the way a volume is displayed, and to change the levels of opacity & positioning of each.

Approach/Methods Cont’d The Volumes module also enables users to choose the appropriate color map, manually adjust window/level sets and display a histogram which plots the number of pixels against image intensity over a background of the ongoing window/level.

Findings/Discussion Cont’d I. Registration Different scans may be recorded at different angles, with different voxel sizes and use different number of slices. We co-register these scans, brought them into spatial alignment using mutual information optimization.

Findings/Discussion Cont’d Modules used in Registration Transformation module: We used Transformation module to manually create and edit Slicer transformation nodes. Transformation nodes are matrices of data that map the voxels of different volume. They can be both linear or nonlinear warp transformations. We usually specified the T1 volume as the fixed base volume, and the FLAIR as the moving 'transformed' volume. We used the 'Translation' and 'Rotation' tools to manually align every 2 volumes in space coordinates.

Findings/Discussion Cont’d Manual registration module in Slicer

Findings/Discussion Cont’d Resample Scalar/Vector/DWI Volume : This module implements image and vector-image resampling through the use of ITK Transforms. 'Resampling' is performed in space coordinates, not pixel/grid coordinates. It is quite important to ensure that image spacing is properly set on the images involved.

Findings/Discussion Cont’d II. Segmentation Image segmentation is a means to simplify image data by dividing it into useful and relevant segments.

Findings/Discussion Cont’d Hui Li, Jianfei Cai, Thi Nhat Anh nguyen, Jianmin Zheng. A benchmark for semantic Image Segmentation. IEEE ICME 2013

Findings/Discussion Cont’d Segmentation was performed after perfectly co-registering the different sequences loaded on Slicer. Segmentation was carried out in a simple hierarchical model of anatomy, proceeding from the peripheral to the central. Three different structures were segmented and later modeled, namely, edema/invasion, enhancing tumor and necrosis, respectively.

Segmentation FLAIR signal hyperintensity Segmentation of enhancing tumor portion on Post-Gd T1WI One label map showing three tumor compartments

Findings/Discussion Cont’d After segmentation was performed, 3D models were created to give better visualization and understanding of spatial anatomic relationships between different tumor compartments.

Findings/Discussion Cont’d 3D Slicer Editor module: This is a core module for manual and semi-automatic segmentation of images. Some of the tools mimic digital painting applications like Photoshop or GIMP, but Slicer tools work on 3D arrays of voxels rather than on 2D pixels, considering slice thickness and slice spacing. The overall goal is to efficiently and precisely define structures within their volumes as “label map volumes”.

Findings/Discussion Cont’d Label map showing segmented tumor model in 3D slicer.

Findings/Discussion Cont’d These label maps can be used for building 3D models, further processing and also for quantification of the segmented portions. Label maps can be exported and re-used in different other softwares, thus, expanding the usage of such image analysis. We used the single label map approach to create models of different tumor volumes on the same label map. Of note, Slicer gives only one label to each pixel.

Findings/Discussion Cont’d Manual segmentation is very precise and accurate in comparison to automated and semi-automated methods but it is more laborous and time consuming. Semi-automated methods such as: Thresholding† Region-growing and competitive region-growing†‡ Atlas-based techniques Edge-detection techniques† Clustering†‡ * †Srinivasan G, Shobha G. Segmentation techniques for target recognition. International Journal of Computers and Communications 2007; 1:313-333. ‡Tirpude N, Welekar R. A study of brain magnetic resonance image segmentation techniques. IJARCCE 2012; 2:958 * Bauer S, May C, Dionysiou D, et al. Multiscale modeling for image analysis of brain tumor studies. IEEE Trans Biomed Eng 2012; 59:25-29

Findings/Discussion Cont’d III. Model Making Using the 'Make Model' tool of the 'Editor' module, the models of edema/invasion, tumor enhancement and necrosis can be generated from the previously performed segmentation. This module is used for loading, saving, layering, changing the appearance of, and organizing 3D surface models. The 'Info' section provides the general information about the models like Surface Area, Volume, etc.

3D Model making in Slicer

Findings/Discussion Cont’d IV. Data Display The Data Module: It gives a complete view of the hierarchical (tree) representation of the MRML Scene and allows you to modify the MRML Scene and the individual "MRML nodes" that represent its constituent data. It allows you to create and edit transformation hierarchy, and to fix and modify models and volumes at a desired registration under transform nodes.

Findings/Discussion Cont’d Obtaining the volumes: After the models of edema, tumor and necrosis have been generated from the segmentation, the corresponding volumes can be calculated and noted from the ‘Label statistics' module. Label Statistics module is used for obtaining image and volume properties from the label maps

Summary/Conclusion This quantitative approach minimizes intra-reader and inter-reader subjectivity and allows more objectivity. Quantitative imaging provides a noninvasive method for initial characterization and assessment of abnormalities and evaluating progression/regression and therapy response.

Summary/Conclusion The complementary information provided by Quantitative Imaging will help improve outcome prediction and evaluation of tumor response to therapy, especially, with certain pathologies where progression is very hard to be detected on longitudinal MRI scans.

Summary/Conclusion Future directives in quantitative image analysis will focus on : reducing manual user interaction, improving accuracy and reproducibility of the automated and semi-automated techniques Optimization of Segmentation algorithms to suit a wider scope of lesions such as Ground Glass Opacity, Mixed Nodules where contour detection is very challenging.

Summary/Conclusion Large scale comparative studies are needed to compare manual segmentations versus semi-automatic and automatic algorithms, and to compare different segmentation algorithms and softwares.

Answers Zinn et al. in 2011 uncovered which Gene/miRNA that is associated with high FLAIR signal in GBM? FHL2 PDGF POSTN TNFSF4 2. Diehn et al. discovered the association between high contrast enhancement to necrosis ratio and which of the following? CAV1 EGFR PGK VEGF

Thank you