Computers in Biology and Medicine

Computed tomography (CT) is one of the most common medical imaging modalities and the main technology used in radiomics research, the computational voxel-level analysis of medical images. Analysis of CT images is vulnerable to the effects of dental artifacts (DA) caused by metal implants or fillings. Running automated analysis pipelines with uncurated datasets can reduce performance and hamper future reproducibility on new datasets. This work introduces a new tool to detect the location and magn...

PurposeConvolutional neural networks (CNNs) have been proposed for super-resolution in CT, but training of CNNs requires high-resolution reference data. Higher spatial resolution can also be achieved using deconvolution, but conventional deconvolution approaches amplify noise. We develop a CNN that mitigates increasing noise and that does not require higher-resolution reference images. MethodsOur model includes a noise reduction CNN and a deconvolution CNN that are separately trained. The noise...

Computed tomography (CT) is an extensively used imaging modality capable of generating detailed images of a patients internal anatomy for diagnostic and interventional procedures. High-resolution volumes are created by measuring and combining information along many radiographic projection angles. In current medical practice, single and dual-view two-dimensional (2D) topograms are utilized for planning the proceeding diagnostic scans and for selecting favorable acquisition parameters, either manu...

PurposeTo improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions. MethodsDeep learning models are developed and tested, with two feature extraction methods and an end-to-end t...

Understanding the causal effects of diseases on body organs through medical imaging is crucial for advancing research and improving clinical outcomes. This paper introduces a novel causal inference framework, Heterogeneous Causal Disease Pattern Detection (HCDPD), designed to map the complex causal pathways from early-stage diseases to latent disease patterns and their manifestation in organs as observed in later-stage medical images. HCDPD serves as a potential outcome framework for multivariat...

Reduction of projection views in X-ray computed tomography (CT) can protect patients from over exposure to ionizing radiation, thus is highly attractive for clinical applications. However, image reconstruction for sparse-view CT which aims to produce decent images from few projection views remains a challenge. To address this, we propose a Residual-guided Golub-Kahan Iterative Reconstruction Technique (RGIRT). RGIRT utilizes an inner-outer dual iteration framework, with a flexible least square Q...

The field of radiomics is at the forefront of personalized medicine. However, there are concerns regarding the robustness of its features against multiple medical imaging parameters and the performance of the predictive models built upon them. Therefore, our review aims to identify image perturbation factors (IPF) that most influence the robustness of radiomic features in biomedical research. We also provide insights into the validity and discrepancy of different methodologies applied to investi...

BackgroundBreast cancer stands as the primary reason for fatality in female patients from cancer worldwide. The diagnostic precision of ultrasound imaging depends on operator skills because it lacks invasive procedures but remains accessible through wide availability. The medical field needs automated systems which provide explainable results to help doctors achieve better breast lesion diagnosis accuracy. ObjectivesThis research presents a new deep learning diagnostic system based on MobileNet...

PurposeTo develop and evaluate a novel double bowtie filter integrating a K-edge material layer with a conventional Teflon filter for pediatric spectral computed tomography (CT). The proposed design aims to enhance spectral signal-to-noise ratio (SNR) and spectral separation while maintaining radiation dose levels suitable for pediatric imaging. MethodsA simulation framework was set up and used to model a rapid kVp-switching CT system operating at 70/110 kVp with realistic tube power and geomet...

MedSegBench is a comprehensive benchmark designed to evaluate deep learning models for medical image segmentation across a wide range of modalities.. This benchmark includes 35 datasets with over 60,000 images, covering modalities such as ultrasound, MRI, and X-ray. It addresses challenges in medical imaging, such as variability in image quality and dataset imbalances, by providing standardized datasets with train/validation/test splits. The benchmark supports binary and multi-class segmentation...

This article discusses the effect of segregation of histopathology images data into three sets; training set for training machine learning model, validation set for model selection and test set for testing model performance. We found that one must be cautious when segregating histological images data (slides) into training, validation and test sets because subtle mishandling of data can introduce data leakage and gives illusively good results on the test set. We performed this study on gene muta...

BackgroundSegmentation is a routine, yet time-consuming and subjective step in the analysis of positron emission tomography (PET) images. Automatic methods to do it have been suggested, but recent method development has focused on supervised approaches. The previously published unsupervised segmentation methods for PET images are outdated for the arising dynamic human total-body PET images now enabled by the evolving scanner technology. MethodsIn this study, we introduce an unsupervised general...

Malignant brain tumors are generally classified to be extremely aggressive and often can be fatal when not met with immediate action. Glioblastoma Multiforme is the most common type of malignant tumor found in the brain and is extremely aggressive. For this reason, advanced detection of malignant brain tumors is necessary for optimal mitigation. Conversely, the classification of tumors during Medical Resonance Imaging can be difficult due to bodily movements resulting in the movement of the tumo...

BackgroundAccurate assessment of the severity of central canal stenosis (CCS) on lumbar spine MRI is critical for clinical decision-making. We evaluated deep learning models for automated CCS grading on sagittal T2-weighted MRI, focusing on uncertainty quantification to improve clinical reliability. MethodsUsing a retrospective cohort from the LumbarDISC dataset (1,974 patients), we compared multiple deep learning architectures for three-level CCS classification (normal / mild, moderate, severe...

BackgroundBreast microcalcification diagnostics are challenging due to their subtle presentation, overlapping with benign findings, and high inter-reader variability, often leading to unnecessary biopsies. While deep learning (DL) models - particularly deep convolutional neural networks (DCNNs) - have shown potential to improve diagnostic accuracy, their clinical application remains limited by the need for large annotated datasets and the "black box" nature of their decision-making. PurposeTo d...

The detection of mitotic figures in histological tumor images plays a vital role in the decision-making of the appropriate therapy. However, tissue preparation and image acquisition methods degrade the performances of the deep learning-based approaches for mitotic figures detection. MItosis DOmain Generalization challenge addresses the domain-shift problem of this detection task. This work presents our approach based on preprocessing homogenizers to tackling this problem.

Saliency methods, which "explain" deep neural networks by producing heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. Although many saliency methods have been proposed for medical imaging interpretation, rigorous investigation of the accuracy and reliability of these strategies is necessary before they are integrated into the clinical setting. In this work, we quantitatively evaluate...

Fractures, often resulting from trauma, overuse, or osteoporosis, pose diagnostic challenges due to their variable clinical manifestations. To address this, we propose a deep learning-based decision support system to enhance the efficacy of fracture detection in radiographic imaging. For the purpose of our study, we utilized 720 annotated musculoskeletal (MSK) X-rays from the MURA dataset, augmented by bounding box-level annotation, for training the YOLO (You Only Look Once) model. The models pe...

The scarcity and imbalance of medical image datasets hinder the development of robust computer-aided diagnosis (CAD) systems for breast cancer. This study explores the application of advanced generative models, based on generative artificial intelligence (GenAI), for the synthesis of digital breast ultrasound images. Using a hybrid Conditional Variational Autoencoder-Wasserstein Generative Adversarial Network (CVAE-WGAN) architecture, we developed a system to generate high-quality synthetic imag...

ObjectiveWe extend existing techniques by using generative adversarial network (GAN) models to reduce the appearance of cast shadows in radiographs across various age groups. Materials and MethodsWe retrospectively collected 12000 adult and pediatric wrist radiographs, evenly divided between those with and without casts. The test subset consisted of 100 radiographs with cast and 100 without cast. We extended the results from a previous study that employed CycleGAN by enhancing the model using a...