Inference With Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences
Segmenting organisms or tumors from medical data (e.g., computed tomography volumetric images, ultrasound, or magnetic resonance imaging images/image sequences) is one of the fundamental tasks in medical image analysis and diagnosis, and has received long-term attentions. This paper studies a novel computational framework of interactive segmentation for extracting liver tumors from image sequences, and it is suitable for different types of medical data. The main contributions are twofold. First, we propose a collaborative model to jointly formulate the tumor segmentation from two aspects: 1) region partition and 2) boundary presence. The two terms are complementary but simultaneously competing: the former extracts the tumor based on its appearance/texture information, while the latter searches for the palpable tumor boundary. Moreover, in order to adapt the data variations, we allow the model to be discriminatively trained based on both the seed pixels traced by the Lucas-Kanade algorithm and the scribbles placed by the user. Second, we present an effective inference algorithm that iterates to: 1) solve tumor segmentation using the augmented Lagrangian method and 2) propagate the segmentation across the image sequence by searching for distinctive matches between images. We keep the collaborative model updated during the inference in order to well capture the tumor variations over time. We have verified our system for segmenting liver tumors from a number of clinical data, and have achieved very promising results. The software developed with this paper can be found at http://vision.sysu.edu.cn/projects/med-interactive-seg/.