Fast aberrant crypt foci segmentation on the GPU

Aberrant crypt foci (ACF) are thought to be one of the earliest manifestations of colorectal cancer (CRC). We propose a parallel signal processing algorithm (based on a fast approach of the active contour without edges model) to segment in vivo ACF, from stained endoscopic images, to serve as a diagnosis/prognosis auxiliary. In order to tackle this challenge, we successfully overcame several obstacles, involving the adaptation of the algorithm and data structures to a parallel environment. This algorithm was developed using the compute unified device architecture (CUDA) interface, which allows programming the graphics processing units (GPU). We show that it is possible to process four frames of 1 Mega pixel images per second using a conventional GPU. The GPU speeds up the in vivo ACF segmentation more than 40×, regarding to CPUs, and paves the way for real-time segmentation.