This paper presents AFMUNet, an Adaptive Filter-Based Frequency Modulation U-Net for Optical Coherence Tomography Angiography (OCTA) segmentation. The model addresses the challenge of segmenting both small blood vessels and larger vascular structures, which exhibit significant variations in scale, contrast, and connectivity. To tackle these issues, AFMUNet achieves an image-sized receptive field while capturing global dependencies, enhancing performance across diverse vessel sizes. Specifically, it incorporates the Fast Fourier Transform (FFT) applied to feature maps across multiple scales of a UNet-like architecture. This component is crucial for identifying critical global frequency patterns, enabling the model to represent the intricate details of small vessels alongside larger ones. Simultaneously, a lightweight attention block is employed to learn adaptive frequency filters from the FFT-derived representations. This mechanism selectively emphasizes transferable frequency components essential for highlighting small and thin vessels while suppressing noise and less informative features that could detract from the segmentation of large vascular structures. Experimental evaluations demonstrate that AFMUNet outperforms state-of-the-art models, achieving mean Dice and mean Intersection over Union (IoU) scores of 91.69% and 84.65%, respectively. These results underscore its robustness and superior ability to accurately segment OCTA images, effectively addressing the dual challenge of capturing both fine-grained and coarse vascular details.

This study proposes a novel deep learning-based automatic segmentation system for accurately delineating pulmonary regions in 3D computed tomography (CT) scans. First, a modified local binary pattern, called cylinder binary pattern (CBP), is introduced, which utilizes concentric cylinders at different radii, to effectively capture intricate textural details at multiple levels. Then, a 3D encoder-decoder deep learning-based network, called VX-Net, is proposed specifically to accurately segment pulmonary regions within 3D CT scans. This network incorporates the hierarchical transformers into its encoder architecture to significantly improve feature extraction process. These transformers replicate Transformer model by integrating 3D convolutions with both larger and smaller kernels. While this network is used for segmenting pulmonary regions, it can also be adapted for various other segmentation tasks. The proposed system is evaluated on 3D CT scans of 26 patients with three different severity levels of COVID-19, using four distinct metrics. These include Dice similarity coefficient (DSC), overlap coefficient, Hausdorff distance (HD), and absolute volume difference (AVD). The proposed system shows remarkable performance, achieving scores of 97.63±0.98%, 95.38±1.86%, 1.99±1.9, and 3.01±1.15, respectively. When compared to various state-of-the-art segmentation methods, the proposed segmentation system showcases its ability to accurately segment both normal and pathological pulmonary regions.

This paper introduces a novel framework for renal segmentation of kidney transplant patients suspected of renal rejection. The framework applies image processing techniques for texture analysis utilizing a modified Pix 2 Pix GAN model to capture the varied kidney shapes in the dataset of 36 subject volumes acquired using BOLD MRI scans. For this problem, we built a framework that analyzes the kidney texture based on four steps: (i) calculate the average CDF for each case to map CDF values to their corresponding intensities for contrast enhancement (ii) extract the region of interest for the kidney to focus on the kidney structure, (iii) calculate the probability maps using the histograms of the contours for the kidney and non-kidney regions, (iv) Create a common-layer across the dataset using the masks by calculating the average of the pixel values of the images to accommodate the shared information within the mask images. Finally, stack the three layers to have the RGB channels contain relevant information about the renal dataset as input for the modified GAN model. The proposed framework achieved an average accuracy and Dice Similarity Coefficient: $90.3 \%$, and $83.1 \%$, respectively. The framework’s primary results underscore its efficiency in providing segmentation for renal diagnosis.

Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (R2*). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm2), while the R2* values were extracted from 30 blood oxygen leveldependent MRI (BOLD-MRI) scans at 5 different echo times (2ms,7ms, 12ms, 17ms, and 22ms). The clinical sources included serum creatinine (SCr) and creatinine clearance (CrCl). First, the kidney was segmented through the RT-CAD system using a geometric deformable model called a level-set method. Second, both ADCs and R2* were estimated for common patients (N=30) and then were integrated with the corresponding SCr and CrCl. Last, these integrated biomarkers were considered the discriminatory features to be used as trainers and testers for future deep learning-based classifiers such as stacked auto-encoders (SAEs). We used a k-fold cross-validation criteria to evaluate the RT-CAD system diagnostic performance, which achieved the following scores: 93.3%, 90.0%, and 95.0% in terms of accuracy, sensitivity, and specificity in differentiating between acute renal rejection (AR) and non-rejection (NR). The reliability and completeness of the RT-CAD system was further accepted by the area under the curve score of 0.92. The conclusions ensured that the presented RT-CAD system has a high reliability to diagnose the status of the renal transplant in a non-invasive way.

The purpose of this work is to develop a computer-aided diagnosis (CAD) system for detecting and localizing prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) acquired at five distinct b-values. The first step in the proposed system depends on nonnegative matrix factorization (NMF) to fuse intensity features of prostate voxels, spatial features of neighboring voxels, and shape prior features to guide the evolution of a level set function for accurate prostate segmentation. The second step in the proposed system involves calculating the apparent diffusion coefficient (ADC) maps of the segmented prostate regions as a discriminating feature between malignant and healthy cases. These ADC maps are used in the last step of the CAD system to train a convolutional neural network (CNN)-based model to identify the ADC maps with malignant tumors. To evaluate the accuracy of the system, 50% of the ADC maps are randomly chosen to train the CNN-model while the second 50% of the ADC maps are used to evaluate the accuracy of the trained model. The proposed CAD system resulted in an average area under the receiver operating characteristic curve (AUC) of 0.93 at the five b-values.

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant functions. In this paper, a computer-aided diagnostic (CAD) system is developed to assess renal transplant functionality, which integrates both clinical and diffusion MRI -derived markers extracted from 4D DW-MRI (i.e. 3D + b-value). To extract the DW-MR image-markers, our framework performs multiple image processing steps, including kidney segmentation using a level-set approach and estimation of image-markers. To extract these image-markers, apparent diffusion coefficients (ADCs) are estimated from the segmented DW-MRIs and cumulative distribution functions (CDFs) of the ADCs are constructed at different b-values (i.e. gradient field strengths and duration). Finally, these markers (i.e. CDFs) are integrated with clinical biomarkers (e.g., creatinine clearance and serum plasma creatinine) to assess transplant status using stacked auto-encoders with non-negativity constraints based on deep learning classification approach. Our CAD system consists of two consecutive classification stages. The first stage classifier achieved a 96% accuracy, a 95% sensitivity, and a 100% specificity in distinguishing non-rejection (NR) from dysfunctional (DF) transplanted kidneys. Additionally, an overall accuracy of 94% has been obtained in the second stage in separating DF to acute rejection (AR) and different renal disease (DRD) transplants. Our preliminary results hold strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.

The ultimate goal of this paper is to develop a novel personalized comprehensive computer aided diagnostic (CAD) system for precise diagnosis of autism spectrum disorder (ASD) based on the 3D shape analysis of the cerebral cortex (Cx), To achieve the main goal of the proposed system, we used structural MRI modality (sMRI) to be able to extract the shape features of the brain cortex. After segmenting the brain cortex from sMRI, we used a spherical harmonics analysis to measure the surface complexity, in addition to studying surface curvatures. Finally, a multi-stage deep network based on several autoencoders and softmax classifiers is constructed to provide the final global diagnosis. The presented CAD system was tested on several datasets, achieving an average accuracy of 92.15%. In addition to its global diagnostic accuracy, the local diagnostic accuracies of the most significant areas also demonstrated the ability of the proposed system to construct very promising local maps of ASD-related brain abnormalities, which can be considered an important step towards personalized medicine for autistic individuals.

This paper introduces a novel shape-based computer-aided diagnosis (CAD) system using magnetic resonance (MR) brain images for autism diagnosis at different life stages. To improve the classification robustness, the system fuses the shape features extracted from the cerebral cortex (Cx) and cerebral white matter (CWM). Fusion is conducted based on the findings suggesting that Cx changes in autism are related to CWM abnormalities. The CAD system starts with segmenting Cx and CWM using a 3D joint model that combines intensity, shape, and spatial information. Then, Spherical Harmonic (SPHARM) is applied to the re-constructed meshes of Cx to derive 4 metrics for each mesh point; normal curvature, mean curvature, gaussian curvature, and Cx surface reconstruction error. To analyze the CWM shape, distance maps of its gyri are computed and three more shape features are extracted for these gyri. Finally, all the extracted shape features are fed to a multi-level deep network for feature fusion and diagnosis. The CAD system has been evaluated using subjects from the ABIDE database (8–12.8 years), achieving an accuracy of 93%, and from NDAR/Pitt database (16–51 years), achieving an accuracy of 97%. Also in order to show the capability of the system for early diagnosis, it has been tested on NDAR/IBIS database for infants, resulting in an accuracy of 85%. These initial results on the 3 databases hold the promise of efficient autism diagnosis.

The proliferation of cell phones has led to an ever increasing number of inappropriate interruptions. Context-aware telephony applications, in which callers are provided with context information about the receivers, has been proposed as a solution for this problem. This approach, however, raises many privacy issues that may render it infeasible. In this paper, we report on an in-situ study of user privacy preferences and patterns of sharing different types of context information with different social relations. We found that participants disclosed their context information generously, suggesting that context-aware telephony is not only feasible, but also desirable. Our data shows a distinct sharing pattern across social relations and different types of context information. We discuss the implications of the results for designers of context-aware telephony in particular and context-aware applications in general.