  We customize an end-to-end image compression framework for retina OCT images
based on deep convolutional neural networks (CNNs). The customized compression
scheme consists of three parts: data Preprocessing, compression CNNs, and
reconstruction CNNs. Data preprocessing module reduces the speckle noise of the
OCT images and the segments out the region of interest. We added customized
skip connections between the compression CNNs and the reconstruction CNNs to
reserve the detail information and trained the two nets together with the
semantic segmented image patches from data preprocessing module. To train the
two networks sensitive to both low frequency information and high frequency
information, we adopted an objective function with two parts: A PatchGAN
discriminator to judge the high frequency information and a differentiable
MS-SSIM penalty to evaluate the low frequency information. The proposed
framework was trained and evaluated on a publicly available OCT dataset. The
evaluation showed above 99% similarity in terms of multi-scale structural
similarity (MS-SSIM) when the compression ratio is as high as 40. Furthermore,
the reconstructed images of compression ratio 80 from the proposed framework
even have better quality than that of compression ratio 20 from JPEG by visual
comparison. The testing result outperforms JPEG in term of both of MS-SSIM and
visualization, which is more obvious as the increase of compression ratio. Our
preliminary result indicates the huge potential of deep neural networks on
customized medical image compression.
