Distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images

Abstract

Background: Automated image analysis on virtual slides is evolving rapidly and will play an important role in the future of digital pathology. Due to the image size, the computational cost of processing whole slide images (WSIs) in full resolution is immense. Moreover, image analysis requires well focused images in high magnification.

Methods: We present a system that merges virtual microscopy techniques, open source image analysis software, and distributed parallel processing. We have integrated the parallel processing framework JPPF, so batch processing can be performed distributed and in parallel. All resulting meta data and image data are collected and merged. As an example the system is applied to the specific task of image sharpness assessment. ImageJ is an open source image editing and processing framework developed at the NIH having a large user community that contributes image processing algorithms wrapped as plug-ins in a wide field of life science applications. We developed an ImageJ plug-in that supports both basic interactive virtual microscope and batch processing functionality. For the application of sharpness inspection we employ an approach with non-overlapping tiles. Compute nodes retrieve image tiles of moderate size from the streaming server and compute the focus measure. Each tile is divided into small sub images to calculate an edge based sharpness criterion which is used for classification. The results are aggregated in a sharpness map.

Results: Based on the system we calculate a sharpness measure and classify virtual slides into one of the following categories - excellent, okay, review and defective. Generating a scaled sharpness map enables the user to evaluate sharpness of WSIs and shows overall quality at a glance thus reducing tedious assessment work.

Conclusions: Using sharpness assessment as an example, the introduced system can be used to process, analyze and parallelize analysis of whole slide images based on open source software.

Figure 1

ImageJ virtual microscope. ImageJ was upgraded to a virtual microscope by connecting a multi format WSI streaming server as an image data provider.

Figure 2

System overview. A) The ImageJ microscope can be used for rapid prototyping purposes. B) An image processing job is divided into several tasks. Image tiles are processed on a grid of standard PCs (compute nodes). Each node receives image tiles and processes predefined plug-ins. Results are merged using a domain specific implementation. C) Application to sharpness assessment.

Figure 3

Sharpness map. Output of sharpness assessment procedure. Each pixel represents the result of a sub tile with a dimension of 128x128 pixels (green – sharp tile, red – blurred tile, light green – sharp tile with high percentage of background, light red – blurred tile with high percentage of background, yellow – medium sharp tile, gray – background).

Figure 4

Processing time. Line chart of total processing time versus number of compute nodes and tissue area given in gigapixels. Time is measured in hours, minutes and seconds h:mm:ss.