A generative model for brain tumor segmentation in multi-modal images

Abstract

We introduce a generative probabilistic model for segmentation of tumors in multi-dimensional images. The model allows for different tumor boundaries in each channel, reflecting difference in tumor appearance across modalities. We augment a probabilistic atlas of healthy tissue priors with a latent atlas of the lesion and derive the estimation algorithm to extract tumor boundaries and the latent atlas from the image data. We present experiments on 25 glioma patient data sets, demonstrating significant improvement over the traditional multivariate tumor segmentation.

Fig. 1

Graphical model for the proposed segmentation approach. Voxels are indexed with i, the channels are indexed with c. The known prior π_k determines the label k of the normal, healthy tissue. The latent atlas α determines the channel-specific presence of tumor t. Normal state k, tumor state t, and intensity distribution parameters θ jointly determine the multi-modal image observations y. Observed (known) quantities are shaded. The tumor segmentation aims to estimate $p(t_i^c\mid \mathit{y})$, along with the segmentation of healthy tissue p(k_i|y).

Fig. 2

Examples of channel-specific segmentation results for four different modalities, in two patients. The outlines of regions with $p(t_i^c=1\mid {\mathit{y}}_i;\widehat{\mathit{\theta }},\widehat{\mathit{\alpha }})>0.5$ are shown in red. The proposed method localizes the tumor reliably in post-therapeutic images (below), where surgery has led to significant deviations from normalcy for healthy tissues.

Fig. 3

Sensitivity to the MRF parameter β. Indicated are the median (solid line) and the interquartile ranges of the average Dice scores of all 25 data set. While some regularization is beneficial, the segmentation performance is relatively insensitive to the choice of the only model parameter β.

Fig. 4

Benefits of the channel-specific segmentation. Boxplots show median, quartiles and outliers for the Dice scores of all 25 subjects, for all four modalities. Our channel-wise segmentation (c, green) improves over both multiple univariate (u, blue) and multivariate (m, red) segmentation, both in the absolute terms (left) and with respect to patient-specific differences (right). The right figure shows c–u and c–m.