A Clinically Explainable AI-Based Grading System for Age-Related Macular Degeneration Using Optical Coherence Tomography

We propose an automated, explainable artificial intelligence (xAI) system for age-related macular degeneration (AMD) diagnosis. Mimicking the physician's perceptions, the proposed xAI system is capable of deriving clinically meaningful features from optical coherence tomography (OCT) B-scan images to differentiate between a normal retina, different grades of AMD (early, intermediate, geographic atrophy (GA), inactive wet or active neovascular disease [exudative or wet AMD]), and non-AMD diseases. Particularly, we extract retinal OCT-based clinical imaging markers that are correlated with the progression of AMD, which include: (i) subretinal tissue, sub-retinal pigment epithelial tissue, intraretinal fluid, subretinal fluid, and choroidal hypertransmission detection using a DeepLabV3+ network; (ii) detection of merged retina layers using a novel convolutional neural network model; (iii) drusen detection based on 2D curvature analysis; (iv) estimation of retinal layers' thickness, and first-order and higher-order reflectivity features. Those clinical features are used to grade a retinal OCT in a hierarchical decision tree process. The first step looks for severe disruption of retinal layers' indicative of advanced AMD. These cases are analyzed further to diagnose GA, inactive wet AMD, active wet AMD, and non-AMD diseases. Less severe cases are analyzed using a different pipeline to identify OCT with AMD-specific pathology, which is graded as intermediate-stage or early-stage AMD. The remainder is classified as either being a normal retina or having other non-AMD pathology. The proposed system in the multi-way classification task, evaluated on 1285 OCT images, achieved 90.82% accuracy. These promising results demonstrated the capability to automatically distinguish between normal eyes and all AMD grades in addition to non-AMD diseases.

A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging

Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In this paper, a multiparametric computer-aided diagnostic (MP-CAD) system is developed to differentiate between BC staging, especially T1 and T2 stages, using T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted (DW) MRI. Our framework starts with the segmentation of the bladder wall (BW) and localization of the whole BC volume (V_t) and its extent inside the wall (V_w). Our segmentation framework is based on a fully connected convolution neural network (CNN) and utilized an adaptive shape model followed by estimating a set of functional, texture, and morphological features. The functional features are derived from the cumulative distribution function (CDF) of the apparent diffusion coefficient. Texture features are radiomic features estimated from T2W-MRI, and morphological features are used to describe the tumors' geometric. Due to the significant texture difference between the wall and bladder lumen cells, V_t is parcelled into a set of nested equidistance surfaces (i.e., iso-surfaces). Finally, features are estimated for individual iso-surfaces, which are then augmented and used to train and test machine learning (ML) classifier based on neural networks. The system has been evaluated using 42 data sets, and a leave-one-subject-out approach is employed. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) are 95.24%, 95.24%, 95.24%, and 0.9864, respectively. The advantage of fusion multiparametric iso-features is highlighted by comparing the diagnostic accuracy of individual MRI modality, which is confirmed by the ROC analysis. Moreover, the accuracy of our pipeline is compared against other statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)). Our CAD system is also compared with other techniques (e.g., end-to-end convolution neural networks (i.e., ResNet50).

A New Framework for Performing Cardiac Strain Analysis from Cine MRI Imaging in Mice

Cardiac magnetic resonance (MR) imaging is one of the most rigorous form of imaging to assess cardiac function in vivo. Strain analysis allows comprehensive assessment of diastolic myocardial function, which is not indicated by measuring systolic functional parameters using with a normal cine imaging module. Due to the small heart size in mice, it is not possible to perform proper tagged imaging to assess strain. Here, we developed a novel deep learning approach for automated quantification of strain from cardiac cine MR images. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of the LV cavity and myocardium via a novel FCN architecture. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. Furthermore, we demonstrated that our new algorithm can determine the strain differences between normal and diseased hearts.

EARLY ASSESSMENT OF RENAL TRANSPLANTS USING BOLD-MRI: PROMISING RESULTS

Non-invasive evaluation of renal transplant function is essential to minimize and manage renal rejection. A computer-assisted diagnostic (CAD) system was developed to evaluate kidney function post-transplantation. The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate renal function. BOLD-MRI scans were acquired at five different echo-times (2, 7, 12, 17, and 22) ms from 15 transplant patients. The developed CAD system first segments kidneys using the level-sets method followed by estimation of the amount of deoxyhemoglobin, also known as apparent relaxation rate (R2*). These R2* estimates were used as discriminatory features (global features (mean R2*) and local features (pixel-wise R2*)) to train and test state-of-the-art machine learning classifiers to differentiate between non-rejection (NR) and acute renal rejection. Using a leave-one-out cross-validation approach along with an artificial neural network (ANN) classifier, the CAD system demonstrated 93.3% accuracy, 100% sensitivity, and 90% specificity in distinguishing AR from non-rejection . These preliminary results demonstrate the efficacy of the CAD system to detect renal allograft status non-invasively.

Detection of microspheres in vivo using multispectral optoacoustic tomography

We introduce a new approach to detect individual microparticles that contain NIR fluorescent dye by multispectral optoacoustic tomography in the context of the hemoglobin-rich environment within murine liver. We encapsulated a near infrared (NIR) fluorescent dye within polystyrene microspheres, then injected them into the ileocolic vein, which drains to the liver. NIR absorption was determined using multispectral optoacoustic tomography. To quantitate the minimum diameter of microspheres, we used both colorimetric and spatial information to segment the regions in which the microspheres appear. Regional diameter was estimated by doubling the maximum regional distance. We found that the minimum microsphere size threshold for detection by multispectral optoacoustic tomography images is 78.9 µm.

Automated and accurate carotid bulb detection, its verification and validation in low quality frozen frames and motion video

AIM

Carotid intima-media thickness (cIMT) measurements during clinical trials need to have a fixed reference point (also called as bulb edge points) in the anatomy from which the cIMT can be measured. Identification of the bulb edge points in carotid ultrasound images faces the challenge to be detected automatically due to low image quality and variations in ultrasound images, motion artefacts, image acquisition protocols, position of the patient, and orientation of the linear probe with respect to bulb and ultrasound gain controls during acquisition.

METHODS

This paper presents a patented comprehensive methodology for carotid bulb localization and bulb edge detection as a reference point. The method consists of estimating the lumen-intima borders accurately using classification paradigm. Transition points are located automatically based on curvature characteristics. Further we verify and validate the locations of bulb edge points using combination of several local image processing methods such as (i) lumen-intima shapes, (ii) bulb slopes, (iii) bulb curvature, (iv) mean lumen thickness and its variations, and (v) geometric shape fitting.

RESULTS

Our database consists of 155 ultrasound bulb images taken from various ultrasound machines with varying resolutions and imaging conditions. Further we run our automated system blindly to spot out the bulbs in a mixture database of 336 images consisting of bulbs and no-bulbs. We are able to detect the bulbs in the bulb database with 100% accuracy having 92% as close as to a neurologists's bulb location. Our mean lumen-intima error is 0.0133 mm with precision against the manual tracings to be 98.92%. Our bulb detection system is fast and takes on an average 9 seconds per image for detection for the bulb edge points and 4 seconds for verification/validation of the bulb edge points.

Hepatocyte Lysosomal Membrane Stabilization by Olive Leaves against Chemically Induced Hepatocellular Neoplasia in Rats

Extensive efforts are exerted looking for safe and effective chemotherapy for hepatocellular carcinoma (HCC). Specific and sensitive early biomarkers for HCC still in query. Present work to study proteolytic activity and lysosomal membrane integrity by hepatocarcinogen, trichloroacetic acid (TCA), in Wistar rats against aqueous olive leaf extract (AOLE).TCA showed neoplastic changes as oval- or irregular-shaped hepatocytes and transformed, vesiculated, and binucleated liver cells. The nuclei were pleomorphic and hyperchromatic. These changes were considerably reduced by AOLE. The results added, probably for the first time, that TCA-induced HCC through disruption of hepatocellular proteolytic enzymes as upregulation of papain, free cathepsin-D and nonsignificant destabilization of lysosomal membrane integrity, a prerequisite for cancer invasion and metastasis. AOLE introduced a promising therapeutic value in liver cancer, mostly through elevating lysosomal membrane integrity. The study substantiated four main points: (1) the usefulness of proteolysis and lysosomalmembrane integrity in early prediction of HCC. (2) TCA carcinogenesis is possibly mediated by lysosomal membrane destabilization, through cathepsin-D disruption, which could be reversed by AOLE administration. (3) A new strategy for management of HCC, using dietary olive leaf system may be a helpful phytotherapeutic trend. (4) A prospective study on serum proteolytic enzyme activity may introduce novel diagnostic tools.

Incidental finding of a large chest wall osteosarcoma--a case report

Primary chest wall tumors are rare and primary osteosarcoma of the chest wall is considered as an even rare among the primary chest wall malignant tumors. The main presentation is rapidly expanding painful mass with elevation of alkaline phosphatase. We present a case of a 34 years old male who was found to have an incidental asymptomatic large chest wall mass with normal alkaline phosphatase level. He underwent several radiological diagnostic modalities which showed the extent and delineation of the mass. Complete excision of the mass was achieved and the chest wall defect was reconstructed with Prolene mesh. The histopathology confirmed the diagnosis of osteosarcoma of the chest wall.