In this paper, we present an efficient trainable conditional random field (CRF) model using a newly proposed scale-targeted loss function to improve the segmentation accuracy on tiny blood vessels in 3D medical images. Blood vessel segmentation is still a big challenge in medical image processing field due to its elongated structure and low contrast. Conventional local neighboring CRF model has poor segmentation performance on tiny elongated structures due to its poor capability capturing pairwise potentials. To overcome this drawback, we use a fully-connected CRF model to capture the pairwise potentials. This paper also introduces a new scale-targeted loss function aiming to improve the segmentation accuracy on tiny blood vessels. Experimental results on both phantom data and clinical CT data showed that the proposed approach contributes to the segmentation accuracy on tiny blood vessels. Compared to previous loss function, our proposed loss function improved about 10% sensitivity on phantom data and 14% on clinical CT data.
Chenglong Wang, Masahiro Oda, Yasushi Yoshino, Tokunori Yamamoto, and Kensaku Mori, "Fine segmentation of tiny blood vessel based on full connected conditional random field," Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740K (Presented at SPIE Medical Imaging: February 12, 2018; Published: 2 March 2018); https://doi.org/10.1117/12.2293486.
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