LAUSR

Abstract Accurate cerebrovascular segmentation in Digital Subtraction Angiography (DSA) image, as an indispensable step, can help doctors appropriately estimate the degree of cerebrovascular lesions to avoid misdiagnosis. Because of the complexity of cerebrovascular structure and the uneven distribution of contrast media in DSA, automatic segmentation is a challenging task in clinical diagnosis. In recent years, deep convolutional neural networks (CNN) have outperformed the state-of-art methods and shown great potential for medical image segmentation. This paper proposes a CNN-based segmentation framework Multiscale Dense CNN (MDCNN) to automatically segment cerebral vessel in DSA images. Inspired by U-net, this proposed MDCNN structure is designed as encoder-decoder architecture. Considering that the diameters of blood vessel in cerebrovascular are various, we define a multiscale module to segment cerebral vessel with different diameters. Meanwhile, we redesign the skip connections between encoder and decoder stage to utilize more features from encoder stage. To improve the capability of extracting high-level features, improved dense blocks are introduced. In terms of simplifying parameters, we refer to the idea of deep supervision to make pruning possible. The proposed framework is tested on DCVessel (a DSA cerebrovascular dataset made by our lab). Our proposed method reaches 0.8813, 0.9784, 0.8775, 0.9886, 0.9944 in F1 score, accuracy (Acc), sensitivity (Sen), specificity (Spe) and AUC respectively, outperforming the state-of-art methods. Meanwhile, we evaluate our MDCNN framework on benchmark retinal vessel dataset DRIVE. The promising experiment results demonstrate that proposed MDCNN model is extensible for various vessel segmentation tasks.