Non-rigid Registration for Large Sets of Microscopic Images on Graphics Processors
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Microscopic imaging is an important tool for characterizing tissue morphology and pathology. 3D reconstruction and visualization of large sample tissue structure requires registration of large sets of high-resolution images. However, the scale of this problem presents a challenge for automatic registration methods. In this paper we present a novel method for efficient automatic registration using graphics processing units (GPUs) and parallel programming. Comparing a C++ CPU implementation with Compute Unified Device Architecture (CUDA) libraries and pthreads running on GPU we achieve a speed-up factor of up to 4.11× with a single GPU and 6.68× with a GPU pair. We present execution times for a benchmark composed of two sets of large-scale images: mouse placenta (16K ×16K pixels) and breast cancer tumors (23K ×62K pixels). It takes more than 12 hours for the genetic case in C++ to register a typical sample composed of 500 consecutive slides, which was reduced to less than 2 hours using two GPUs, in addition to a very promising scalability for extending those gains easily on a large number of GPUs in a distributed system.
KeywordsMicroscopic imaging Image registration and segmentation Pattern analysis Feature detection Graphics processors High-performance computing
This work was partially supported by the Ministry of Education of Spain (TIC2003-06623, PR-2007-0014), Junta de Andalucía of Spain (P06-TIC-02109), US NIH grant R01 DC06458-01A1 and the startup fund from the Department of Biomedical Informatics at the Ohio State University, US.
We thank Dr. Gustavo Leone from the Ohio State University Cancer Center for providing us the images from mouse placenta and mouse mammary gland we used during the experiments outlined in this paper. We also thank Dr. Dennis Sessanna and Dr. Donald Stredney from the Ohio Supercomputing Center for providing us access to the BALE visualization cluster where most of our execution times were obtained.
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