Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning

Adversarial learning-based domain adaptation algorithm for intracranial artery stenosis detection on multi-source datasets

Intracranial arterial stenosis (ICAS) is characterized by the pathological narrowing or occlusion of the inner lumen of intracranial blood vessels. However, the retina can indirectly react to cerebrovascular disease. Therefore, retinal fundus images (RFI) serve as valuable noninvasive and easily accessible screening tools for early detection and diagnosis of ICAS. This paper introduces an adversarial learning-based domain adaptation algorithm (ALDA) specifically designed for ICAS detection in multi-source datasets. The primary objective is to achieve accurate detection and enhanced generalization of ICAS based on RFI. Given the limitations of traditional algorithms in meeting the accuracy and generalization requirements, ALDA overcomes these challenges by leveraging RFI datasets from multiple sources and employing the concept of adversarial learning to facilitate feature representation sharing and distinguishability learning. In order to evaluate the performance of the ALDA algorithm, we conducted experimental validation on multi-source datasets. We compared its results with those obtained from other deep learning algorithms in the ICAS detection task. Furthermore, we validated the potential of ALDA for detecting diabetic retinopathy. The experimental results clearly demonstrate the significant improvements achieved by the ALDA algorithm. By leveraging information from diverse datasets, ALDA learns feature representations that exhibit enhanced generalizability and robustness. This makes it a reliable auxiliary diagnostic tool for clinicians, thereby facilitating the prevention and treatment of cerebrovascular diseases.

Automatic Analysis of Transverse Musculoskeletal Ultrasound Images Based on the Multi-Task Learning Model

Musculoskeletal ultrasound imaging is an important basis for the early screening and accurate treatment of muscle disorders. It allows the observation of muscle status to screen for underlying neuromuscular diseases including myasthenia gravis, myotonic dystrophy, and ankylosing muscular dystrophy. Due to the complexity of skeletal muscle ultrasound image noise, it is a tedious and time-consuming process to analyze. Therefore, we proposed a multi-task learning-based approach to automatically segment and initially diagnose transverse musculoskeletal ultrasound images. The method implements muscle cross-sectional area (CSA) segmentation and abnormal muscle classification by constructing a multi-task model based on multi-scale fusion and attention mechanisms (MMA-Net). The model exploits the correlation between tasks by sharing a part of the shallow network and adding connections to exchange information in the deep network. The multi-scale feature fusion module and attention mechanism were added to MMA-Net to increase the receptive field and enhance the feature extraction ability. Experiments were conducted using a total of 1827 medial gastrocnemius ultrasound images from multiple subjects. Ten percent of the samples were randomly selected for testing, 10% as the validation set, and the remaining 80% as the training set. The results show that the proposed network structure and the added modules are effective. Compared with advanced single-task models and existing analysis methods, our method has a better performance at classification and segmentation. The mean Dice coefficients and IoU of muscle cross-sectional area segmentation were 96.74% and 94.10%, respectively. The accuracy and recall of abnormal muscle classification were 95.60% and 94.96%. The proposed method achieves convenient and accurate analysis of transverse musculoskeletal ultrasound images, which can assist physicians in the diagnosis and treatment of muscle diseases from multiple perspectives.

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.

MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis

PURPOSE

The identification of early-stage Parkinson's disease (PD) is important for the effective management of patients, affecting their treatment and prognosis. Recently, structural brain networks (SBNs) have been used to diagnose PD. However, how to mine abnormal patterns from high-dimensional SBNs has been a challenge due to the complex topology of the brain. Meanwhile, the existing prediction mechanisms of deep learning models are often complicated, and it is difficult to extract effective interpretations. In addition, most works only focus on the classification of imaging and ignore clinical scores in practical applications, which limits the ability of the model. Inspired by the regional modularity of SBNs, we adopted graph learning from the perspective of node clustering to construct an interpretable framework for PD classification.

METHODS

In this study, a multi-task graph structure learning framework based on node clustering (MNC-Net) is proposed for the early diagnosis of PD. Specifically, we modeled complex SBNs into modular graphs that facilitated the representation learning of abnormal patterns. Traditional graph neural networks are optimized through graph structure learning based on node clustering, which identifies potentially abnormal brain regions and reduces the impact of irrelevant noise. Furthermore, we employed a regression task to link clinical scores to disease classification, and incorporated latent domain information into model training through multi-task learning.

RESULTS

We validated the proposed approach on the Parkinsons Progression Markers Initiative dataset. Experimental results showed that our MNC-Net effectively separated the early-stage PD from healthy controls(HC) with an accuracy of 95.5%. The t-SNE figures have showed that our graph structure learning method can capture more efficient and discriminatory features. Furthermore, node clustering parameters were used as important weights to extract salient task-related brain regions(ROIs). These ROIs are involved in the development of mood disorders, tremors, imbalances and other symptoms, highlighting the importance of memory, language and mild motor function in early PD. In addition, statistical results from clinical scores confirmed that our model could capture abnormal connectivity that was significantly different between PD and HC. These results are consistent with previous studies, demonstrating the interpretability of our methods.

Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip

Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.

Diagnosing glaucoma on imbalanced data with self-ensemble dual-curriculum learning

Glaucoma diagnosis often suffers from two types of data imbalances: 1) class imbalance, i.e., the non-glaucoma majority cases occupy most of the data; 2) rare cases, i.e., few cases present the uncommon retinopathy e.g., bayoneting or physiologic cupping. This dual-imbalances make glaucoma diagnosis model easy to be dominated by the majority cases but cannot correctly classify the minority and/or rare ones. In this paper, we propose an adaptive re-balancing strategy in the feature space, Self-Ensemble Dual-Curriculum learning (SEDC), to improve the glaucoma diagnosis on imbalanced data by augmenting feature distribution with feature distilling and feature re-weighting. Firstly, the self-ensembling (SEL) is developed to reinforce the discriminative ability of feature representations for the minority class and rare cases by distilling the features learned from the abundant majority cases. Secondly, the dual-curriculum (DCL) is designed to adaptively re-weight the imbalanced data in the feature space to learn a balanced decision function for accurate glaucoma diagnosis. Benefiting from feature distilling and re-weighting, the proposed SEDC fairly represents fundus images, regardless of the majority or rare cases, by augmenting the feature distribution to obtains the optimal decision boundary for accurate glaucoma diagnosis on the imbalanced dataset. Experimental results on three challenging glaucoma datasets show that our SEDC successfully delivers accurate glaucoma diagnosis by the adaptive re-balancing strategy, with the average mean value of Accuracy 0.9712, Sensitivity 0.9520, Specificity 0.9816, AUC 0.9928, F2-score 0.9547. Ablation and comparison studies demonstrate that our method outperforms state-of-the-art methods and traditional re-balancing strategies. The experiment also shows that the adaptive re-balancing strategy proposed in our method provides a more effective training approach with optimal convergence performance. It endows our SEDC a great advantage to handle the disease diagnosis on imbalanced data distribution.

An Effective Approach Based on Deep Residual Google Net Convolutional Neural Network Classifier for the Detection of Glaucoma

Glaucoma is a disease caused by fluid pressure build-up in the inner eye. Early detection of glaucoma is critical as it is expected that 111.8 million people worldwide shall suffer from glaucoma in 2040. In the diagnosis of glaucoma, the use of machine learning method is hoped to be highly promising. This paper provides an important method to master learning to diagnose glaucoma. Initially, human retinal fundus images are preprocessed by means of histogram equalization in order to enhance them. The segmentation is performed by semantic segmentation method, mainly the features are extracted using density with correlation based feature extraction approach. PCA (principal component analysis) methodology is used to choose the most optimal features. Ultimately, through the usage of the Deep residual Google Net CNN Classification method, the retinal image is classified/predicted as regular and abnormal. The Deep residual Google Net CNN classifier is designed to distinguish view patterns with minimal pre-processing from pixel pictures. ORIGA and STARE datasets are used in this work. The findings are then analyzed and contrasted to illustrate the efficacy of the new technique with alternate current techniques. Test accuracy of 99%, Specificity of 98.9% and 100% Sensitivity were achieved. The quantitative results are analyzed for specifications like sensitivity, specificity, accuracy, positive predictive rate, false predictive rate and assured to provide excellent outcomes when compared with traditional methods.

Applications of deep learning in fundus images: A review

The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provided for each task. Finally, limitations common to all tasks are revealed and possible solutions are given. We will also release and regularly update the state-of-the-art results and newly-released datasets at https://github.com/nkicsl/Fundus_Review to adapt to the rapid development of this field.

Evaluation and comparison of global-feature-based and local-feature-based segmentation algorithms in intracranial visual pathway delineation

Intracranial visual pathway is related to the effective transmission of visual signals to brain. It was not only the target organ of diseases but also the organs at risk in radiotherapy thus its delineation plays an important role in both diagnosis and treatment planning. Traditional manual segmentation method suffered from time- and labor- consuming as well as intra- and inter- variability. In order to overcome these problems, state-of-the-art segmentation models were designed and various features were extracted and utilized, but it's hard to tell their effectiveness on intracranial visual pathway delineation. It's because that these methods worked on different dataset and accompanied with different training tricks. This study aimed to research the contribution of global features and local features in delineating the intracranial visual pathway from MRI scans. The two typical segmentation models, 3D UNet and DeepMedic, were chosen since they focused on global features and local features respectively. We constructed the hybrid model through serially connecting the two mentioned models to validate the performance of combined global and local features. Validation results showed that the hybrid model outperformed the individual ones. It proved that multi scale feature fusion was important in improving the segmentation performance.

A novel glaucomatous representation method based on Radon and wavelet transform

Background

Glaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns.

Method

We proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection.

Results

The proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively.

Conclusion

In conclusion, we showed that the proposed method has the capacity as an effective tool for large-scale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.