Compressed single-shot 3D photoacoustic imaging with a single-element transducer.

Three-dimensional (3D) photoacoustic imaging (PAI) can provide rich information content and has gained increasingly more attention in various biomedical applications. However, current 3D PAI methods either involves pointwise scanning of the 3D volume using a single-element transducer, which can be time-consuming, or requires an array of transducers, which is known to be complex and expensive. By utilizing a 3D encoder and compressed sensing techniques, we develop a new imaging modality that is capable of single-shot 3D PAI using a single-element transducer. The proposed method is validated with phantom study, which demonstrates single-shot 3D imaging of different objects and 3D tracking of a moving object. After one-time calibration, while the system could perform single-shot 3D imaging for different objects, the calibration could remain effective over 7 days, which is highly beneficial for practical translation. Overall, the experimental results showcase the potential of this technique for both scientific research and clinical applications.

Multi-scale brain tumor segmentation combined with deep supervision.

PURPOSE: Fully convolutional neural networks (FCNNs) have achieved good performance in the field of medical image segmentation. FCNNs that use multimodal images and multi-scale feature extraction have higher accuracy for brain tumor segmentation. Therefore, we have made some improvements to U-Net for fully automated segmentation of gliomas using multimodal images. And we named it multi-scale dilate network with deep supervision (MSD-Net). METHODS: MSD-Net is a symmetrical structure composed of a down-sampling process and an up-sampling process. In the down-sampling process, we use the multi-scale feature extraction block (ME) to extract multi-scale features and focus on primary features. Unlike other methods, ME consists of dilate convolution and standard convolution. Dilate convolution extracts multi-scale informations and standard convolution merges features of different scales. Hence, the output of the ME contains local information and global information. During the up-sampling process, we add a deep supervision block (DSB), which can shorten the length of back-propagation. In this paper, we pay more attention to the importance of shallow features for feature restoration. RESULTS: Our network validated in the BraTS17's validation dataset. The DSC scores of MSD-Net for complete tumor, tumor core and enhancing tumor were 0.88, 0.81 and 0.78, respectively, which outperforms most networks. CONCLUSION: This study shows that ME enhances the feature extraction ability of the network and improves the accuracy of segmentation results. DSB speeds up the convergence of the network. In addition, we should also pay attention to the contribution of shallow features to feature restoration.