Cardiac functional analysis is important in heart disease diagnosis. Conventional manual segmentation of left ventricle is time consuming and observer dependent. Our proposed Densely Connected Full Convolutional Network (DenseV-Net) enables automatically process medical images. Its densely connected convolutional block consists of residual calculation with Elu used as active function. The results show that the proposed DenseV-Net can efficiently segment left ventricle from cardiac cines with mean DSC of 0.90±0.12, more accurate compared to V-Net (0.85±0.13, P<0.05). The method offers a feasible way for efficient analysis of cardiac function.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords