Abstract
Deep learning (DL) has been widely used in biomedical image segmentation and automatic disease diagnosis, leading to state-of-the-art performance. However, automated cardiac disease diagnosis heavily relies on cardiac segmentation maps from cardiac magnetic resonance (CMR), most current DL segmentation methods, such as 2D convolution on planes, 3D convolution, are not fully applicable to CMR due to loss of spatial structure information or large gap between slices. To make better exploit spatial aspects of the CMR data to improve cardiac segmentation accuracy, we propose a new DL segmentation structure, which consists of a residual convolution neural network for compressing the intra-slice information, and a bidirectional-convolutional long short term memory (Bi-CLSTM) for leveraging the inter-slice contexts. Moreover, automatic disease diagnosis has been conducted using the segmentation maps. Experimental results of the automatic cardiac diagnosis challenge (ACDC) show that our cardiac segmentation structure and disease diagnosis methods have achieved promising results and it can be widely extended to computer-aided diagnosis.
Highlights
Cardiac magnetic resonance (CMR) is the gold standard method for the assessment of cardiac structure and function [1], extracting left and right ventricular ejection fractions (EF), stroke volumes (SV) and left ventricle mass from CMR is usually as the primary step for cardiac disease diagnosis, but most current heart segmentation methods process 3D data in a slice-by-slice fashion, as the result these methods lose the spatial structure information in the original data, so we use the method of combining 2D and temporal information to compensate the spatial aspects loss of the original data in the 2D convolution network
We propose a new Deep learning (DL) segmentation structure, which is constituted by a residual convolution neural network for compressing the intra-slice information, and a Bi-CLSTM [2] for leveraging the inter-slice contexts
RELATED WORK Each frame in the CMR is a 3D structure, current state-ofthe-art methods for 3D biomedical image segmentation (Here, we mainly focus on DL segmentation methods, because they are relevant to our scheme and usually perform well) can be roughly divided into the following five categories. (I) 2D convolution on planes. (II) 3D convolution
Summary
Cardiac magnetic resonance (CMR) is the gold standard method for the assessment of cardiac structure and function [1], extracting left and right ventricular ejection fractions (EF), stroke volumes (SV) and left ventricle mass from CMR is usually as the primary step for cardiac disease diagnosis, but most current heart segmentation methods process 3D data in a slice-by-slice fashion, as the result these methods lose the spatial structure information in the original data, so we use the method of combining 2D and temporal information to compensate the spatial aspects loss of the original data in the 2D convolution network. We propose a new DL segmentation structure, which is constituted by a residual convolution neural network for compressing the intra-slice information, and a Bi-CLSTM [2] for leveraging the inter-slice contexts. We conducted automatic disease diagnosis from the segmentation maps. The intra-slice information is compressed by convolutional neural network. For a set of 3D cardiac data, adjacent frames are similar, so the temporal relationship in Bi-CLSTM are used to simulate intra-slice contexts. Cardiac diseases can be automatically diagnosis from the segmentation maps. Our approach combines automatic cardiac segmentation and disease diagnosis together.
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