Interactive Medical Image Sequence Segmentation
- Liang Lin, Wei Yang, Chenglong Li, Jin Tang, and Xiaochun Cao. Inference with Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences. IEEE Transactions on Cybernetics (T-Cybernetics), 2015.
Fig. 1. Selected results of liver tumor segmentation in CT image slices.
In order to adapt the different appearances of medical data. The inference of image-series segmentation iterates with two steps (Fig. 4):
- We apply the cooperative model for segmentation of the current observed image by employing the Bregman procedure.
- We propagate the segmentation to the following image by searching for distinctive matches between images, while we keep the model updated.
Fig. 6. Results on CT images.
Fig. 7. Results on Ultrasound Image sequences.
|average runtime (s)||2.494||6.635||19.716||65.700||0.565||0.334||0.684|
Table 1. Comparison with the state-of-the-arts.
Table I plots the average accuracy (TP/(TP+FP+FN)) on single image segmentation (dataset: subCT and subUS). Provided the same user interactions, our approach outperforms interactive segmentation method GrabCut , GAC , and DRLSE . Our approach also yield better segmentations with STF . Our framework, however, is more efficient and need less tedious human annotation compared with fully supervised methods. Moreover, our method is per-image-specific, thus adapts various imaging conditions, while fully supervised methods stuck in this situation.
- Y.Y. Boykov and M.-P. Jolly. “Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images”. In Proc. IEEE ICCV, 2001.
- V. Caselles, R. Kimmel, and G. Sapiro. “Geodesic active contours,” IJCV, 22: 61–79, 1997.
- J. Shotton, M. Johnson, R. Cipolla. “Semantic Texton Forests for Image Categorization and Segmentation”. In Proc. IEEE CVPR, 2008.
- C. Li, C. Xu, C. Gui, and M. D. Fox. “Distance Regularized Level Set Evolution and Its Application to Image Segmentation”. IEEE Trans. on image process., 19(12): 3243-3254, 2010.