Sketch-Based Editing Tools for Tumour Segmentation in 3D Medical Images

Abstract

In the past years sophisticated automatic segmentation algorithms for various medical image segmentation problems have been developed. However, there are always cases where automatic algorithms fail to provide an acceptable segmentation. In these cases the user needs efficient segmentation editing tools, a problem which has not received much attention in research. We give a comprehensive overview on segmentation editing for three‐dimensional (3D) medical images. For segmentation editing in two‐dimensional (2D) images, we discuss a sketch‐based approach where the user modifies the segmentation in the contour domain. Based on this 2D interface, we present an image‐based as well as an image‐independent method for intuitive and efficient segmentation editing in 3D in the context of tumour segmentation in computed tomography (CT). Our editing tools have been evaluated on a database containing 1226 representative liver metastases, lung nodules and lymph nodes of different shape, size and image quality. In addition, we have performed a qualitative evaluation with radiologists and technical experts, proving the efficiency of our tools.In the past years sophisticated automatic segmentation algorithms for various medical image segmentation problems have been developed. However, there are always cases where automatic algorithms fail to provide an acceptable segmentation. In these cases the user needs efficient segmentation editing tools, a problem which has not received much attention in research. We give a comprehensive overview on segmentation editing for 3D medical images. For segmentation editing in 2D, we discuss a sketch‐based approach where the user modifies the segmentation in the contour domain. Based on this 2D interface, we present an image‐based as well as an image‐independent method for intuitive and efficient segmentation editing in 3D in the context of tumour segmentation in CT.