Wajid Mumtaz

Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we proposed a 3D-atrous spatial pyramid pooling (ASPP) module integrated with a proposed 3D DensNet encoder–decoder network for volumetric segmentation to segment abdominal organs from CT. The proposed network used a 3D-ASPP block to capture spatial information in multiscale input feature maps from the decoder side. We also proposed a 3D-ASPP block with a 3D DensNet network for automatically processing 3D medical volumetric images. The proposed hybrid network was named 3D-ASPPDN for volumetric segmentation via CT medical imaging. We tested our proposed approach on a public dataset, Thoracic Organs at Risk (SegTHOR) 2019. The proposed solution showed excellent performance in comparison with other existing state-of-the-art DL methods. The proposed method achieved Dice scores 97.89% on the SegTHOR dataset. Results presented that 3D-ASPPDN exhibited enhanced performance in volumetric biomedical segmentation. The proposed model could be used for volumetric segmentation in clinical applications to diagnose problems in multi class organs.

The selection of stimulus contents for neurofeedback has direct implications on the efficacy of neurofeedback therapy. In particular, a suitable selection of stimulus contents facilitates the achievement of sustainability during neurofeedback sessions, which has been considered challenging during clinical practice. To further elaborate this point, this research investigates the efficacy of different neurofeedback stimulus contents (audio, video, and games) for stress mitigation. The effectiveness of the contents was measured by statistically comparing quantitative electroencephalogram (QEEG) features, such as alpha power and alpha asymmetry before and after neurofeedback sessions. In addition, the topographic maps of activities were constructed for a visual description. In this study, 29 study participants were recruited, and the EEG data were recorded during multiple neurofeedback sessions. ANOVA and post hoc testing verified the statistical significance of the results of the various stimulus contents, whereas a t-test verified the significance of stress mitigation because of neurofeedback. The results indicate that games exhibit higher effectiveness than audio and video contents for stress mitigation. In addition, the topographic analysis demonstrates the efficacy of neurofeedback training for stress mitigation. In conclusion, the effects of neurofeedback therapy could be enhanced while selecting suitable stimulus contents for neurofeedback protocols.