Multiscale Dense Convolutional Neural Network for DSA Cerebrovascular Segmentation

Publication date: Available online 17 October 2019Source: NeurocomputingAuthor(s): Cai Meng, Kai Sun, Shaoya Guan, Qi Wang, Rui Zong, Lei LiuABSTRACTAccurate 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 ...

Source: Neurocomputing - October 18, 2019 Category: Neuroscience Source Type: research