Thumbnails for Medical Volume Data Browser

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1 Thumbnails for Medical Volume Data Browser The need and its possible solutions Presented by CASMV () Fei Yang & Dehui Xiang from Institute of Automation Jia Li & Shanqing Huang from Institute of Plasma Physics Chinese Academy of Science

2 A Typical Medical Image Browser

3 The need of thumbnails Traditional browser (without thumbnails) Information it gives Patients IDs, names, Datasets Modality Study Date. Applicability Maybe good for doctors daily use to find dataset of a given patient Not so convenient when you want to find datasets that contains certain kind of pathology characteristic How can thumbnails help

4 The need of thumbnails How can thumbnails help Thumbnails can intuitively depict the content of the datasets Useful when you are searching some kind of common structure across different patients Use cases During review of datasets, when there are too many datasets, and no idea of what each of them contains When researchers go to the doctor s to find the datasets containing the content of their interest The Bottom Line Thumbnails are intuitive, they looks beautiful.

5 The intuitive UI of FastStone Image Viewer

6 Our goal Generate thumbnails for medical volume datasets to make database browsing process more intuitive.

7 Tricky issues A 3D-Volume can be visualized in different ways Slice view Volume Rendering Surface Rendering A 3D scene can be viewed from different view angles Randomly selected slices may be meaningless How can thumbnail best represent the dataset s pathology information.

8 Approaches Thumbnail by marking a representative ROI on a slice Thumbnail by Volume Rendering

9 Approaches Mark a representative ROI on a slice Select a slice that contains structures of interest Locate a representative ROI on the slice Limitation: Structures of interest may locate on different slices Generate Thumbnails by Volume Rendering Transfer-Functions should be generated and applied to expose features of interest. Angle of view should be automatically chosen or dynamically manipulated. A 3D Region-Of-Interest can be helpful

10 VR-Based Thumbnail Generation Static and Dynamic VR-based Thumbnails Static VR-based Thumbnails: Thumbnails are generated one time by a batch program Dynamic VR-based Thumbnails: = Volume rendering of multiple data-sets View-angle can be dynamically adjusted for all datasets

11 Static VR-Based Thumbnails Basic Idea Thumbnails are generated one time as 2D images by a batch program Data-set should be sorted into groups, different data-sets may apply different transfer-functions and view-angle selection strategy

12 Static VR-Based Thumbnails Work Flow Processing Center (A special workstation) Sort datasets into groups Apply a transfer-function for each group Select a view-angle for each dataset automatically Cast rays for each dataset A thumbnail for each dataset Load Datasets Database Write Thumbnails Scanner Visualization Workstation Feed dataset grouping and ROI information to database Load and display thumbnails

13 Dynamic VR-Based Thumbnails Basic Idea Volume Rendering multiple datasets in a grid of tiny views View angles are initialized according to ROI information, and can be dynamically adjusted. User can have more aspects of information of the datasets. Performance Challenges Fast access of datasets from database Interactive rendering of multiple datasets Strategy Down-sample at early stage

14 Dynamic VR-Based Thumbnails Work Flow Processing Center Down-sample Datasets Load Datasets Database Write Down-sampled datasets Scanner Visualization Workstation Feed dataset grouping and ROI information to database Load down-sampled datasets from database Initialize view-angle for each thumbnail,apply a transfer-function for each group User dynamically adjust view-angles and browse the datasets.

15 Performance Test Datasets are down-sampled to as small as 64^3 16 tiny datasets are rendered using CPU-Based Shear-Warp (single thread) and dynamically manipulated in 16 rendering contexts. Rendering each thumbnail costs about 16ms Involvement of message queue causes delay

16 Using 3D Texture Render in multiple contexts (120x120 x16) The graphic card contains 8 GPU cores at 460MHz Render in a shared context (510x510)

17 Conclusion Thumbnails can be helpful when browsing medical image database, however, generation of thumbnails for 3D volume is not so straightforward, especially when we want the thumbnails be meaningful. Various approaches are proposed. Test shows that performance is not a big problem when datasets are reasonably down-sampled. It seems that when using hardware acceleration, better performance can be achieved by sharing the same rendering context. Automatic identification of structures of interest is still challenging. Manually selected ROI can be an alternative.

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