Ultrasound imaging (US) is one of the most commonly used techniques for detecting breast lesions. However, due to the inherent properties of low contrast, speckle noise, and blurred boundaries in B-mode ultrasound images, the performance of breast lesion segmentation was significantly limited. In contrast, original radiofrequency (RF) data contains more detailed information. To enhance the performance of breast lesion segmentation and compensate for the information missing in ultrasound images, this study proposed a radiofrequency and ultrasound image feature attention and fusion network (RUIFAF Net) to comprehensively fuse the complementary information from both modalities. Specifically, we proposed a Feature Attention Module (FAM) and a Multi-level Fusion (MF) method to obtain multi-scale feature information and integrate contextual semantic information, respectively. Experimental results on OASBUD dataset demonstrate that our proposed RUIFAF_Net achieved higher Dice (0.8264 vs. 0.6468-0.8184) and IoU (0.7161 vs. 0.5243-0.7025) compared with the other six advanced methods.

The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering. The fully convolutional network with three convolution blocks is first used to learn the expressive seizure characteristics from EEG data. Then these robust EEG features pertinent to seizures are presented as an input to the Nested Long Short-Term Memory (NLSTM) model to explore the inherent temporal dependencies in EEG signals. Lastly, the high-level features obtained from the NLSTM model are fed into the softmax layer to output predicted labels. The proposed method yields an accuracy range of 98.44-100% in 10 different experiments based on the Bonn University database. A larger EEG database is then used to evaluate the performance of the proposed method in real-life situations. The average sensitivity of 97.47%, specificity of 96.17%, and false detection rate of 0.487 per hour are yielded. For CHB-MIT Scalp EEG database, the proposed model also achieves a segment-level sensitivity of 94.07% with a false detection rate of 0.66 per hour. The excellent results obtained on three different EEG databases demonstrate that the proposed method has good robustness and generalization power under ideal and real-life conditions.

Precise localization of moving targets is essential to increase local control of the cancer via dose escalation while reducing the severity of normal tissue complication. Localization of targets in real time with radio-opaque marker is less favorable considering the excess radiation dose to the patient and potential complications of implantation. Various external surrogates could provide indications of the targets' positions during the breathing process. However, there is a great deal of uncertainty in the correlation between external surrogates and internal target positions/trajectory during respiratory cycles. In order to address this problem, we have developed an algorithm that automatically establishes correspondences between the fluoroscopic sequence frames taken from the patient on the day of treatment and the various phases of a 4DCT planning data set. Image based mapping/synchronization procedure is performed using an underlying Markov model established for the breathing process. The mapping procedure is formulated as an optimization process and is solved efficiently using a dynamic programming technique. Results on the phantom, synthetic, and real patient data demonstrate the effectiveness of the proposed method in coping with respiratory correlation variations. The approach could primarily be used for automatic gating interval adaptation in the gated radiotherapy.