Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures

Hip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis.We describe a novel network analysis methodology for microcomputed tomography (micro-CT) images of human trabecular bone.We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences were discovered, particularly in the proximal third of the femoral head, where HOA networks demonstrated elevated numbers of edges, vertices, and graph components. When further differentiating healthy joint and HOA networks, the latter showed fewer small-world network properties, due to decreased clustering coefficient and increased characteristic path length. Furthermore,we found that HOA networks had reduced length of edges, indicating the formation of compressed trabecular structures. In order to assess our network approach,we developed a deep learningmodel for classifying HOA and control cases, and we fed it with two separate inputs: (i) micro-CT images of the trabecular bone, and (ii) the network extracted from them. The model with plain micro-CT images achieves 74.6% overall accuracy while the trained model with extracted networks attains 96.5% accuracy. We anticipate our findings to be a starting point for a novel description of bone microstructure in HOA, by considering the phenomenon from a graph theory viewpoint.Mohsen Dorraki, Dzenita Muratovic, Anahita Fouladzadeh, Johan W. Verjans, Andrew Allison, David M. Findlay and Derek Abbot

3D-printed micro lens-in-lens for in vivo multimodal microendoscopy

Published online: March 1, 2022Multimodal microendoscopes enable co-located structural and molecular measurements in vivo, thus providing useful insights into the pathological changes associated with disease. However, different optical imaging modalities often have conflicting optical requirements for optimal lens design. For example, a high numerical aperture (NA) lens is needed to realize high-sensitivity fluorescence measurements. In contrast, optical coherence tomography (OCT) demands a low NA to achieve a large depth of focus. These competing requirements present a significant challenge in the design and fabrication of miniaturized imaging probes that are capable of supporting high-quality multiple modalities simultaneously. An optical design is demonstrated which uses two-photon 3D printing to create a miniaturized lens that is simultaneously optimized for these conflicting imaging modalities. The lens-in-lens design contains distinct but connected optical surfaces that separately address the needs of both fluorescence and OCT imaging within a lens of 330 µm diameter. This design shows an improvement in fluorescence sensitivity of >10x in contrast to more conventional fiber-optic design approaches. This lens-in-lens is then integrated into an intravascular catheter probe with a diameter of 520 µm. The first simultaneous intravascular OCT and fluorescence imaging of a mouse artery in vivo is reported.Jiawen Li, Simon Thiele, Rodney W. Kirk, Bryden C. Quirk, Ayla Hoogendoorn, Yung Chih Chen, Karlheinz Peter, Stephen J. Nicholls, Johan W. Verjans, Peter J. Psaltis, Christina Bursill, Alois M. Herkommer, Harald Giessen, and Robert A. McLaughli

Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images

Available online 18 August 2023Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applied in disease screening problems because the training sets available for those problems usually contain only normal images. However, the exclusive reliance on normal images may result in the learning of ineffective low-dimensional image representations that are not sensitive enough to detect and segment unseen abnormal lesions of varying size, appearance, and shape. Pre-training UAD methods with self-supervised learning, based on computer vision techniques, can mitigate this challenge, but they are suboptimal because they do not explore domain knowledge for designing the pretext tasks, and their contrastive learning losses do not try to cluster the normal training images, which may result in a sparse distribution of normal images that is ineffective for anomaly detection. In this paper, we propose a new self-supervised pre-training method for MIA UAD applications, named Pseudo Multi-class Strong Augmentation via Contrastive Learning (PMSACL). PMSACL consists of a novel optimisation method that contrasts a normal image class from multiple pseudo classes of synthesised abnormal images, with each class enforced to form a dense cluster in the feature space. In the experiments, we show that our PMSACL pre-training improves the accuracy of SOTA UAD methods on many MIA benchmarks using colonoscopy, fundus screening and Covid-19 Chest X-ray datasets.Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneir

Effects of CPAP treatment on cardiac structure and function in individuals with obstructive sleep apnea and cardiovascular disease: a prospective 6-month randomised control trial

Background: Obstructive sleep apnea (OSA) is associated with an increased risk of cardiovascular events. The influence of continuous positive airway pressure (CPAP) on cardiac function remains uncertain. Purpose: To prospectively determine the effects of CPAP on cardiovascular function, as measured by cardiac magnetic resonance imaging (CMR) as a sub-study of the international SAVE trial (NCT00738179). Methods: Participants with OSA and established cardiovascular disease were randomized to CPAP treatment plus usual cardiovascular care or Usual Care alone. Primary outcomes were defined as change in ventricular ejection fraction (EF) and stroke volume (SV) between baseline and 6-month follow-up both groups. Secondary outcomes included other ventricular parameters including volumes, mass, and strain, and atrial parameters. Results: 140 participants were included; mean CPAP adherence in those allocated to receive the treatment was 4.31±2.45 hours per night. Most were male (91%) and had moderate-severe OSA with minimal daytime sleepiness. There were no significant differences in left or right ventricular EF between groups after 6 months of treatment. There was an 8.5 mL increase in LV SV (95% CI; [4.3–12.6], p<0.001) and a 7.7 mL increase in RV SV (95% CI; [3.4–12.0], p<0.001) in the CPAP group compared to Usual Care. CPAP also affected left and right ventricular EDV, RV strain, and atrial parameters. Conclusions: In the first prospective CMR imaging study of patients with OSA and cardiovascular disease, CPAP treatment did not change EF after 6 months, but did have significant effects on other parameters of cardiac function.K. Franke, K.A. Loffler, S.J. Nicholls, C.S. Anderson, B.R. Cowan, R.D. McEvoy, A.A. Young, J.W. Verjan

Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in medical images

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual labelling of abnormal samples and the subsequent issues involved in training with extremely class-imbalanced data. Further, UAD approaches can potentially detect and localise any type of lesions that deviate from the normal patterns. One significant challenge faced by UAD methods is how to learn effective low-dimensional image representations to detect and localise subtle abnormalities, generally consisting of small lesions. To address this challenge, we propose a novel self-supervised representation learning method, called Constrained Contrastive Distribution learning for anomaly detection (CCD), which learns fine-grained feature representations by simultaneously predicting the distribution of augmented data and image contexts using contrastive learning with pretext constraints. The learned representations can be leveraged to train more anomaly-sensitive detection models. Extensive experiment results show that our method outperforms current state-of-the-art UAD approaches on three different colonoscopy and fundus screening datasets. Our code is available at https://github.com/tianyu0207/CCD.Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh, and Gustavo Carneir

Photoshopping colonoscopy video frames

The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps – such training set is considerably less expensive to obtain, compared to the 2-class data set mentioned above. During inference, the system is only able to reconstruct frames without polyps, and when it tries to reconstruct a frame with polyp, it automatically removes (i.e., photoshop) it from the frame – the difference between the input and reconstructed frames is used to detect frames with polyps. We name our proposed model as anomaly detection generative adversarial network (ADGAN), comprising a dual GAN with two generators and two discriminators. To test our framework, we use a new colonoscopy data set with 14317 images, split as a training set with 13350 images without polyps, and a testing set with 290 abnormal images containing polyps and 677 normal images without polyps. We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.Yuyuan Liu, Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W. Verjans, Gustavo Carneir

The development of tumour vascular networks

The growth of solid tumours relies on an ever-increasing supply of oxygen and nutrients that are delivered via vascular networks. Tumour vasculature includes endothelial cell lined angiogenesis and the less common cancer cell lined vasculogenic mimicry (VM). To study and compare the development of vascular networks formed during angiogenesis and VM (represented here by breast cancer and pancreatic cancer cell lines) a number of in vitro assays were utilised. From live cell imaging, we performed a large-scale automated extraction of network parameters and identified properties not previously reported. We show that for both angiogenesis and VM, the characteristic network path length reduces over time; however, only endothelial cells increase network clustering coefficients thus maintaining smallworld network properties as they develop. When compared to angiogenesis, the VM network efficiency is improved by decreasing the number of edges and vertices, and also by increasing edge length. Furthermore, our results demonstrate that angiogenic and VM networks appear to display similar properties to road traffic networks and are also subject to the well-known Braess paradox. This quantitative measurement framework opens up new avenues to potentially evaluate the impact of anti-cancer drugs and anti-vascular therapies.Anahita Fouladzadeh, Mohsen Dorraki, Kay Khine Myo Min, Michaelia P. Cockshell, Emma J. Thompson, Johan W. Verjans, Andrew Allison, Claudine S. Bonder and Derek Abbot

Ultrathin monolithic 3D printed optical coherence tomography endoscopy for preclinical and clinical use

Preclinical and clinical diagnostics increasingly rely on techniques to visualize internal organs at high resolution via endoscopes. Miniaturized endoscopic probes are necessary for imaging small luminal or delicate organs without causing trauma to tissue. However, current fabrication methods limit the imaging performance of highly miniaturized probes, restricting their widespread application. To overcome this limitation, we developed a novel ultrathin probe fabrication technique that utilizes 3D microprinting to reliably create side-facing freeform micro-optics (<130 µm diameter) on single-mode fibers. Using this technique, we built a fully functional ultrathin aberration-corrected optical coherence tomography probe. This is the smallest freeform 3D imaging probe yet reported, with a diameter of 0.457 mm, including the catheter sheath. We demonstrated image quality and mechanical flexibility by imaging atherosclerotic human and mouse arteries. The ability to provide microstructural information with the smallest optical coherence tomography catheter opens a gateway for novel minimally invasive applications in disease.Jiawen Li, Simon Thiele, Bryden C. Quirk, Rodney W. Kirk, Johan W. Verjans, Emma Akers ... et al

Quantitative intravascular biological fluorescence-ultrasound imaging of coronary and peripheral arteries <em>in vivo</em>.

Aims: (i) to evaluate a novel hybrid near-infrared fluorescence - intravascular ultrasound (NIRF-IVUS) system in coronary and peripheral swine arteries in vivo; (ii) to assess simultaneous quantitative biological and morphological aspects of arterial disease. Methods and results: Two 9F/15MHz peripheral and 4.5F/40MHz coronary near-infrared fluorescence (NIRF)-IVUS catheters were engineered to enable accurate co-registrtation of biological and morphological readings simultaneously in vivo. A correction algorithm utilizing IVUS information was developed to account for the distance-related fluorescence attenuation due to through-blood imaging. Corrected NIRF (cNIRF)-IVUS was applied for in vivo imaging of angioplasty-induced vascular injury in swine peripheral arteries and experimental fibrin deposition on coronary artery stents, and of atheroma in a rabbit aorta, revealing feasibility to intravascularly assay plaque structure and inflammation. The addition of ICG-enhanced NIRF assessment improved the detection of angioplasty-induced endothelial damage compared to standalone IVUS. In addition, NIRF detection of coronary stent fibrin by in vivo cNIRF-IVUS imaging illuminated stent pathobiology that was concealed on standalone IVUS. Fluorescence reflectance imaging and microscopy of resected tissues corroborated the in vivo findings. Conclusions: Integrated cNIRF-IVUS enables simultaneous co-registered through-blood imaging of disease related morphological and biological alterations in coronary and peripheral arteries in vivo. Clinical translation of cNIRF-IVUS may significantly enhance knowledge of arterial pathobiology, leading to improvements in clinical diagnosis and prognosis, and helps to guide the development of new therapeutic approaches for arterial diseases