TSI-Net: A Timing Sequence Image Segmentation Network for Intracranial
  Artery Segmentation in Digital Subtraction Angiography
Lemeng Wang Wentao Liu Weijin Xu Haoyuan Li Huihua Yang Feng Gao
Cerebrovascular disease is one of the major diseases facing the world today.
Automatic segmentation of intracranial artery (IA) in digital subtraction
angiography (DSA) sequences is an important step in the diagnosis of vascular
related diseases and in guiding neurointerventional procedures. While, a single
image can only show part of the IA within the contrast medium according to the
imaging principle of DSA technology. Therefore, 2D DSA segmentation methods are
unable to capture the complete IA information and treatment of cerebrovascular
diseases. We propose A timing sequence image segmentation network with U-shape,
called TSI-Net, which incorporates a bi-directional ConvGRU module (BCM) in the
encoder. The network incorporates a bi-directional ConvGRU module (BCM) in the
encoder, which can input variable-length DSA sequences, retain past and future
information, segment them into 2D images. In addition, we introduce a sensitive
detail branch (SDB) at the end for supervising fine vessels. Experimented on
the DSA sequence dataset DIAS, the method performs significantly better than
state-of-the-art networks in recent years. In particular, it achieves a Sen
evaluation metric of 0.797, which is a 3% improvement compared to other
methods.