Fig. 1. Selected results of liver tumor segmentation in CT image slices.
Segmenting organisms or tumors from medical data (e.g. ultrasound videos, CT or MRI volumes) is one of the fundamental tasks in medical informatics and diagnosis, and thus receives long-term attentions. We studies a general framework of interactive image sequence segmentation that can be adaptively applied for different types of medical data. Our system is able to accurately segments an image based on very few user scribbles (Fig. 2) and automatically propagates the segmentation into consecutive image series, resulting in the spatio-temporal tumor volume extraction.
We propose a cooperative model of a dual form that formulates the tumor segmentation from two aspects: region partition and boundary localization. The two terms are complementary but simultaneously competing; the former extracts the tumor by its appearance/texture difference against surrounding background and the latter searches for the palpable tumor boundary (Fig. 3). Moreover, we allow the model discriminatively trained based on the user placing scribbles.
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.
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