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Medical Image Segmentation is a useful application for medical image analysisincluding detecting diseases and abnormalities in imaging modalities such asMRI, CT etc. Deep learning has proven to be promising for this task but usuallyhas a low accuracy because of the lack of appropriate publicly availableannotated or segmented medical datasets. In addition, the datasets that areavailable may have a different texture because of different dosage values orscanner properties than the images that need to be segmented. This paperpresents a StyleGAN-driven approach for segmenting publicly available largemedical datasets by using readily available extremely small annotated datasetsin similar modalities. The approach involves augmenting the small segmenteddataset and eliminating texture differences between the two datasets. Thedataset is augmented by being passed through six different StyleGANs that aretrained on six different style images taken from the large non-annotateddataset we want to segment. Specifically, style transfer is used to augment thetraining dataset. The annotations of the training dataset are hence combinedwith the textures of the non-annotated dataset to generate new anatomicallysound images. The augmented dataset is then used to train a U-Net segmentationnetwork which displays a significant improvement in the segmentation accuracyin segmenting the large non-annotated dataset.

Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data Augmentation