Transrectal ultrasound (TRUS) images have real-time and low-cost advantages. It is essential for preoperative diagnosis and intraoperative treatment of the prostate to segment prostates from TRUS images. In this paper, an Adaptive Detail Compensation Network (ADC-Net) for 3D prostate segmentation is proposed, which utilizes the convolutional neural networks (CNN) in deep learning to realize the automatic segmentation of TRUS images. The proposed method is consisting of a U-Net-based backbone network, a detail compensation module, three spatial-based attention modules, and an aggregation fusion module. A pre-trained ResNet-34 as the detail compensation module is utilized to compensate for the loss of detailed information caused by the down-sampling process of the U-Net encoder. The proposed method uses the spatial-based attention module to introduce multilevel features to refine single-layer features, thereby suppressing the useless background influence and enriching the contextual information of the foreground. Finally, to obtain a predicted prostate, the aggregation fusion module fuses refined single-layer features to further enrich the prostate semantic information and filter out other irrelevant information in TRUS images. Furthermore, a deep supervision mechanism applied in our method also plays an irreplaceable role in network training. Experimental results show that the proposed ADC-Net has achieved satisfactory results in the 3D TRUS image segmentation of prostates, providing accurate detection of prostate regions.
Image acquired during free breathing using contrast enhanced ultrasound (CEUS) hepatic perfusion imaging exhibits a periodic motion pattern. It needs to be compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. A respiratory motion compensation strategy for CEUS imaging by using image clustering is proposed in this work. The proposed strategy separated the dual mode image to tissue image and contrast image firstly. Then, the image subsequences based on the tissue image are determined by using sparse subspace clustering (SSC) method. Finally, the motion compensated contrast images are acquired by using the position mapping. The strategy was tested on ten CEUS hepatic perfusion image sequences. Quantitative and visual comparisons demonstrate that the proposed strategy can compensate the misalignment of ultrasound hepatic perfusion image sequence caused by respiratory motion in free-breathing.
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