Automatic segmentation of the left ventricle (LV) in four-chamber view images is critical for computer-aided cardiac disease diagnosis. The complex structure of the cardiac image and the encoder-decoder networks may cause coarse segmentation results. High-accuracy LV segmentation is still a challenge with existing automatic LV segmentation methods . In this paper, we propose a slide deep reinforcement learning segmentation network for pixelwise LV segmentation. The main architecture of the slide reinforcement learning networks consists of a slider item combined state, a group of morphology transforming actions and an agent network. The specifically designed reinforcement learning state comprises an image item and a slider item, which contains both original image information and network act information. The reinforcement learning actions proposed in this paper enable accurate and fast formulation of the binary segment result for each frame by controlling the length and location of the slider. Additionally, the confidence branch proposed in our experiment provides a continuous frame series environment, and the identification algorithm avoids losing the segmentation target. The segmentation result reveals that the proposed method outperforms FCN, SegNet, U-Net and TransUnet. The IoU improved by 23.01 % , 15.4 % , 11.24 % and 6.9 % . Additionally, we demonstrate how the proposed method can be used as a semisupervised method, which is more convenient for the image annotation process.

In this paper, an efficient geography registration method for INSAR coherent change detection is proposed. In the algorithm, considering Terra-SAR image, firstly we do geography registration on two images by oversampling the geography information in master image, then do coarse and fine registration. In coarse registration, find the range and azimuth shifts by calculating the cross correlation of the intensity sub-images that around ground control points (GCPs). In fine registration, calculate intensity cross correlation of sub-images that around GCPs to find the maximum of correlation map for getting range and azimuth shifts. In coarse and fine registration algorithms, time complexity is determined by the size of sub-image and number of GCPs. The proposed algorithm improves the efficiency while keeping the precision of registration. Experimental results obtained on acquired by Terra-SAR images confirm the effectiveness of the proposed approach.