Medical Image Segmentation Using Interactive Refinement

Image segmentation is an important and challenging area of research in computer vision with various applications in medical imaging. Image segmentation refers to the process of partitioning an image into meaningful parts having similar attributes. Traditional manual segmentation approaches rely on human expertise to outline object boundaries in images which is a tedious and expensive process. In recent years, Deep Convolutional Neural Networks have demonstrated excellent performance in tasks such as detection, localization, recognition and segmentation of objects. However, these models require a large set of labeled training data which is difficult to obtain for medical images. To solve this problem, interactive segmentation techniques can be used to serve as a trade-off between fully automated and manual approaches. This allows a human expert in the loop as a form of guidance and refinement together with deep neural networks. This thesis proposes an interactive training strategy for segmentation, where a robot-user is utilized during training to mimic an actual annotator and provide corrections to the predicted masks by drawing scribbles. These scribbles are then used as supervisory signals and fed to the network; which interactively refines the segmentation map through several iterations of training. Further, the conducted experiments using various heuristic click strategies demonstrate that user interaction in the form of curves inside the organ of interest achieve optimal editing performance. Moreover, by using the popular image segmentation architectures based on U-Net as base models, segmentation performance is further improved; signifying that the accuracy gain of the interactive correction conform to the accuracy of the initial segmentation map.