Semi-automatic medical image segmentation with adaptive local statistics in Conditional Random Fields framework

Abstract

Planning radiotherapy and surgical procedures usually require onerous manual segmentation of anatomical structures from medical images. In this paper we present a semi-automatic and accurate segmentation method to dramatically reduce the time and effort required of expert users. This is accomplished by giving a user an intuitive graphical interface to indicate samples of target and non-target tissue by loosely drawing a few brush strokes on the image. We use these brush strokes to provide the statistical input for a Conditional Random Field (CRF) based segmentation. Since we extract purely statistical information from the user input, we eliminate the need of assumptions on boundary contrast previously used by many other methods, A new feature of our method is that the statistics on one image can be reused on related images without registration. To demonstrate this, we show that boundary statistics provided on a few 2D slices of volumetric medical data, can be propagated through the entire 3D stack of images without using the geometric correspondence between images. In addition, the image segmentation from the CRF can be formulated as a minimum s-t graph cut problem which has a solution that is both globally optimal and fast. The combination of a fast segmentation and minimal user input that is reusable, make this a powerful technique for the segmentation of medical images.

Figure 1

The process of segmenting a stack of medical images in our method. (1) On one of the images, the user specifies the seed pixels interactively by using brush strokes. At this time no statistics is available for segmentation. Once the initial result is satisfactory, pixels along the boundary are sampled. (2) On the subsequent image slices, the boundary term is estimated from the samples on the training slice. The regional term is also estimated from the new brush strokes on the image slice being segmented. Note that the humane interactions (brush strokes) are significantly reduced. (3) Users can always re-train the model if the boundary statistics is no longer applicable.

Figure 2

The CRF graphical model for labeling x given 2D image y, where {y, x_i, x_j} is one of the cliques. The conditional probability p(x | y) can be factorized by clique potentials.

Figure 3

The edge cost assignment. The cost of the min s–t cut in the graph minimizes our energy function E in (4).

Figure 4

An s–t cut on a 3×3 image. White dots are target pixels and black dots are non-target pixels. The dotted lines are edges being cut. The cost of the cut is sum of the edges’ cost being cut.

Figure 5

Segmentation results from the phantom image. The first row is clean image and the second row is the same image with Gaussian noises. The target is inner rectangle. (a) GCHC. (b)(d) GCHC with extra brushes. (e) GCHC with regional term. (c)(f) Our method GCPE.

Figure 6

The contours extracted from our method (cyan) and the contours drawn by an experienced physician (red) from 9 of 65 CT slices segmented. The middle slice in red frame is the slice used for training and its statistics for boundary is used for all 65 slices.

Figure 7

CT slices where the boundary of liver becomes blurred. Red: ground truth, Orange: RG, Blue: LS, Filled Cyan: GCPE. Boundary leakage is severe in RG and LS.