3D image segmentation is an important and ubiquitous task in many fields. Image segmentation is the process of dividing an image into a plurality segments which correspond to different characteristics. For example, a computed tomography image of a femur may be segmented into bone and background segments. Non-limiting uses of image segmentation include:                simplifying and/or enhancing images;        identifying and/or highlighting objects in images;        creating representations of objects depicted in images (e.g., 3D model reconstructions).        
Image segmentation can be represented in various ways, such as by description of segment boundaries (e.g., by curves, surfaces, and the like), and values assigned to image regions (e.g., values assigned to pixels, voxels or other coordinates corresponding to image locations). Values assigned to image regions may comprise labels (e.g., indicating that the labelled image region has, or has been determined to have, a particular characteristic), or probabilities (e.g., indicating the probability that the corresponding region has a particular characteristic). For example, an image segmentation may assign every voxel in a 3D image a label having a value of 0 or 1 to indicate that the voxel belongs to the background or a foreground object, or may assign every voxel in a 3D image a probability value in the range of 0 to 1 to indicate that probability that the voxel belongs to a particular object. In some cases, there may be multiple objects (e.g. multiple types of tissues) within a given 3D image and an image segmentation may assign multiple probabilities to each voxel in the image, with each probability representing a likelihood that the voxel corresponds to a particular one of the multiple objects.
A wide variety of 3D image segmentation techniques is known. Image segmentation techniques can be characterized by the degree of user interaction they require. At one extreme, image segmentation may be a completely manual process, in which all segmentation decisions are made by a user. An example of completely manual 3D image segmentation is manual slice-by-slice segmentation, in which a 3D image is divided into component image planes, and each plane segmented by a human operator. Manual slice-by-slice segmentation is widely recognized as impractical, being too tedious, time consuming, expensive, and suffering from high inter- and intra-operator variability.
At the other extreme, image segmentation may be a completely automatic process, in which all segmentation decisions are made by a machine (e.g., a programmed computer). Known fully-automated segmentation techniques generally lack the accuracy and robustness required for segmenting images having variable structure(s) and/or for segmentation applications where accuracy is paramount (e.g., medical images depicting anatomical structures affected by subject diversity and/or pathology). Fully-automated segmentation techniques are typically only suitable for images with common or predictable structure(s) and generally require careful tuning according to image properties to operate acceptably.
In between these extremes lie semi-automated segmentation techniques, which combine user input with automated segmentation decision making. Some semi-automated segmentation techniques are iterative. Iterative semi-automated segmentation techniques typically involve iteratively repeating the blocks of: providing user input; and automatically generating a segmentation based on the user input. User input to iterative semi-automated segmentation techniques may be based on a previous segmentation together with available image data and may be intended to yield a subsequent segmentation that is improved over the previous segmentation. In one exemplary semi-automated segmentation process, an initial iteration involves: obtaining user input based on the image data; and generating an initial segmentation based on the user input and the image data. Second and subsequent iterations may then involve: obtaining further user input based on the image data together with the previous segmentation; and generating a segmentation based on the user input, the image data and the previous segmentation. In other embodiments, semi-automated segmentation processes may involve automatically generating a segmentation in a first iteration and then obtaining user input for second and subsequent iterations. Semi-automated segmentation techniques may also be referred to as interactive segmentation techniques.
FIG. 1 is a schematic diagram of an exemplary interactive segmentation technique 10 for segmenting 3D image data 14. An image segmentation 12 and a portion (e.g. a slice) of 3D image data 14 may be displayed to an operator O on a display 16. Operator O provides user input 18 at user interface 20. User input 18, which may be based on 3D image data 14 and/or a previous segmentation 12, is provided to a segmentation engine 22. Segmentation engine 22 automatically generates a new segmentation 12 which may be based on user input 18, 3D image data 14 and a previous segmentation 12. The process of providing user input 18 based on image segmentation 12 and then generating a revised segmentation 12 constitutes a loop 24. Loop 24 may be repeated, with user data 18 provided in each iteration accumulating in segmentation engine 22, such that the revised segmentations 12 produced in successive iterations of loop 24 are based on increasing amounts of user input 18. To the extent that user input 18 is reliable, segmentation 12 can be expected to improve with successive iterations of loop 24.
A problem with interactive segmentation techniques is that it may not be clear to a human operator what particular user input would most improve the segmentation. Though an understanding of the algorithm used to automatically generate a segmentation may provide some sense of what sort of input would more likely improve a segmentation, such understanding may be uncommon among those who perform segmentation. For example, doctors and clinicians involved in performing segmentation of medical images may not appreciate the intricacies involved in 3D segmentation algorithms. Even those with such an understanding may fail to provide optimal or near optimal input due to the complexity of the algorithm, difficulty perceiving features of a segmentation, and the like. Accordingly there is need for methods and apparatus that improve the quality of user input provided to interactive 3D image segmentation techniques.
The foregoing examples of the related art and limitations related thereto are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the drawings.