BACKGROUND

Segmentation can be used to quantify regions or areas of an image. For example, segmentation can be used to quantify areas in tissue images to assist in medical diagnoses or medical management. For accurate quantification, it is preferred that the boundaries between the tissue(s) of interest are continuous, without gaps.

SUMMARY

Image segmentation can be used to quantify areas or regions in tissue images to assist in medical diagnoses and medical management. Existing approaches implement non-probabilistic models, such as graph search and graph cut approaches, which require substantial and clinical expertise to model the tissue of interest adequately. Other approaches use probabilistic models. The features to create these probabilistic models again need to be defined manually, which is tedious and prone to error. Ideally there would be a segmentation technique that has high performance and learns these features and models from training data, while enforcing continuity of the output segmentation surfaces.

Deep learning approaches, and specifically convolutional neural networks (CNN) have been shown repeatedly to outperform classical image analysis techniques and even human experts for detection and recognition and detection of objects in images, and even in medical images.

However, there is no straightforward approach to use CNN for image segmentations, as the output of standard CNNs is not constrained to be continuous. Required is a deep learning based approach to segmentation that learns both the features and models from training data without intervention by a human expert, while enforcing continuity of the output segmentation surfaces.

