Infarct region segmentation in rat brain T2 MR images after stroke based on fully convolutional networks

Stroke remains one of the most life-threatening diseases around the world. Rodent stroke models have been widely adopted in experimental ischemia studies for decades. Magnetic resonance imaging (MRI) has been shown effective to reveal the stroke region and associated tissues in many animal studies. Extraction of the infarct regions in rat brain MR images after stroke is crucial for further investigation such as neuro damage analysis and behavior examination. This paper is in an attempt to develop a computer-aided infarct segmentation algorithm based on a fully convolutional network (FCN) for rat stroke model analyses in MR images. In our approach, the entire procedure is divided into two major phases: skull stripping and infarct segmentation. The purpose of the skull stripping process is to provide a clean brain region, from which the infract segmentation is executed. The same FCN is applied to both phases but with different training images and segmentation purposes. Our FCN model consists of 33 convolutional layers, 5 maximum pooling layers, and 5 upsampling layers. The residual network is introduced to the FCN architecture for updating the weights and the batch normalization strategy is exploited to reduce the gradient vanishing problem. To evaluate the proposed FCN framework, 35 subjects of T2-weighted MR images of the rat brain acquired from National Taiwan University, Taipei, Taiwan were utilized. Preliminary experimental results indicated that our method produced high segmentation accuracy regarding skull stripping (Dice = 98.12) and infarct segmentation (Dice = 80.47) across a number of rat brain MR image volumes.