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Background: Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) diagnosis, though confirming specific biomarkers remains challenging. Our proposed Multi-Scale Self-Attention Network (MUSAN) enhances classification of cognitively normal (CN) and AD individuals, distinguishing stable (sMCI) from progressive mild cognitive impairment (pMCI). Objective: This study leverages AD structural atrophy properties to achieve precise AD classification, combining different scales of brain region features. The ultimate goal is an interpretable algorithm for this method. Methods: The MUSAN takes whole-brain sMRI as input, enabling automatic extraction of brain region features and modeling of correlations between different scales of brain regions, and achieves personalized disease interpretation of brain regions. Furthermore, we also employed an occlusion sensitivity algorithm to localize and visualize brain regions sensitive to disease. Results: Our method is applied to ADNI-1, ADNI-2, and ADNI-3, and achieves high performance on the classification of CN from AD with accuracy (0.93), specificity (0.82), sensitivity (0.96), and area under curve (AUC) (0.95), as well as notable performance on the distinguish of sMCI from pMCI with accuracy (0.85), specificity (0.84), sensitivity (0.74), and AUC (0.86). Our sensitivity masking algorithm identified key regions in distinguishing CN from AD: hippocampus, amygdala, and vermis. Moreover, cingulum, pallidum, and inferior frontal gyrus are crucial for sMCI and pMCI discrimination. These discoveries align with existing literature, confirming the dependability of our model in AD research. Conclusion: Our method provides an effective AD diagnostic and conversion prediction method. The occlusion sensitivity algorithm enhances deep learning interpretability, bolstering AD research reliability. [ABSTRACT FROM AUTHOR]

Susceptibility-weighted images (SWIs) have recently been confirmed to be more sensitive to acute ischemic stroke than diffusion weighted images in the form of the presence of small veins shortly after the symptom onset. Accurate segmentation of small veins in SWIs is critical for quantitative diagnosis, individual therapy and outcome prediction of acute ischemic stroke. It is challenging to segment veins in SWIs as they exhibit substantial variability and intensity inhomogeneity within a patient and among patients, which may even be hard for experts to delineate manually. A deep convolutional neural network is proposed to segment veins in SWIs with two main contributions: dense connection to concatenate feature maps from preceding layers to enhance network performance, and a hybrid loss function comprising of classification accuracy and global region overlap terms to handle class imbalance. Experiments have been conducted on 10 consecutive patients with acute ischemic stroke using leave-one-out validation, yielding the best Dice coefficient (0.756 ± 0.043) (p < 0.001) as compared with 3 relevant methods. The proposed method could provide a potential tool to quantify veins in SWIs with accuracy to assist decision making especially for thrombolytic therapy. [ABSTRACT FROM AUTHOR]

• Bone age assessment (BAA) could attain state-of-the-art accuracy through pre-training via optimal data augmentation, and the training procedure with supervised training and gender last training strategy. • Data augmentation based on combination of optimal transport and generative adversarial network with cosine distance metric can enhance BAA better than existing data augmentation methods. • Traditional image processing can be employed to replace the manual delineation of references which are utilized to train deep learning. • It might be generally applicable for multi-label classification with the label carrying less information being supervised last in the network. Bone age assessment (BAA) is widely used in determination of discrepancy between skeletal age and chronological age. Manual approaches are complicated which require experienced experts, while existing automatic approaches are perplexed with small and imbalanced samples which is a big challenge in deep learning. In this study, we proposed a new deep learning based method to improve the BAA training in both pre-training and training architecture. In pre-training, we proposed a framework using a new distance metric of cosine distance in the framework of optimal transport for data augmentation (CNN-GAN-OTD). In the training architecture, we explored the order of gender label and bone age information, supervised and semi-supervised training. We found that the training architecture with the CNN-GAN-OTD based data augmentation and supervised gender-last classification with supervised Inception v3 network yielded the best assessment (mean average error of 4.23 months). The proposed data augmentation framework could be a potential built-in component of general deep learning networks and the training strategy with different label order could inspire more and deeper consideration of label priority in multi-label tasks. [ABSTRACT FROM AUTHOR]

Lung nodules are small, round, or oval-shaped masses of tissue in the lung region. Early diagnosis and treatment of lung nodules can significantly improve the quality of patients’ lives. Because of their small size and the interlaced nature of chest anatomy, detection of lung nodules using different medical imaging techniques becomes challenging. Recently, several methods for computer aided diagnosis (CAD) were proposed to improve the detection of lung nodules with good performances. However, the current methods are unable to achieve high sensitivity and high specificity. In this paper, we propose using deep feature fusion from the non-medical training and hand-crafted features to reduce the false positive results. Based on our experimentation of the public dataset, our results show that, the deep fusion feature can achieve promising results in terms of sensitivity and specificity (69.3% and 96.2%) at 1.19 false positive per image, which is better than the single hand-crafted features (62% and 95.4%) at 1.45 false positive per image. As it stands, fusion features that were used to classify our candidate nodules have resulted in a more promising outcome as compared to the single features from deep learning features and the hand-crafted features. This will improve the current CAD method based on the use of deep feature fusion to more effectively diagnose the presence of lung nodules. [ABSTRACT FROM AUTHOR]

• Augmented reality techniques can help surgeons to see the internal anatomy from laparoscopic video images. • Deep learning methods are successfully used in dense stereo reconstructions of liver surfaces and in liver segmentations of preoperative CT images. • The augmented reality prototype system was validated ex vivo and in vivo and the accuracy is comparable to the state-of-the-art. Understanding the three-dimensional (3D) spatial position and orientation of vessels and tumor(s) is vital in laparoscopic liver resection procedures. Augmented reality (AR) techniques can help surgeons see the patient's internal anatomy in conjunction with laparoscopic video images. In this paper, we present an AR-assisted navigation system for liver resection based on a rigid stereoscopic laparoscope. The stereo image pairs from the laparoscope are used by an unsupervised convolutional network (CNN) framework to estimate depth and generate an intraoperative 3D liver surface. Meanwhile, 3D models of the patient's surgical field are segmented from preoperative CT images using V-Net architecture for volumetric image data in an end-to-end predictive style. A globally optimal iterative closest point (Go-ICP) algorithm is adopted to register the pre- and intraoperative models into a unified coordinate space; then, the preoperative 3D models are superimposed on the live laparoscopic images to provide the surgeon with detailed information about the subsurface of the patient's anatomy, including tumors, their resection margins and vessels. The proposed navigation system is tested on four laboratory ex vivo porcine livers and five operating theatre in vivo porcine experiments to validate its accuracy. The ex vivo and in vivo reprojection errors (RPE) are 6.04 ± 1.85 mm and 8.73 ± 2.43 mm, respectively. Both the qualitative and quantitative results indicate that our AR-assisted navigation system shows promise and has the potential to be highly useful in clinical practice. [ABSTRACT FROM AUTHOR]