Accurate digital heart mesh models are crucial for cardiac electromechanical simulations, commonly derived from Cardiac Magnetic Resonance (CMR) or Computed Tomography (CT) imaging. CMR offers high tissue contrast but suffers from large inter-...

In radiation therapy (RT), the reliance on pre-treatment computed tomography (CT) images encounter challenges due to anatomical changes, necessitating adaptive planning. Daily cone-beam CT (CBCT) imaging, pivotal for therapy adjustment, falls ...

Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs can lead to false-negative/positive and leakage issues in advanced methods. Additionally, ...

Cone Beam Computed Tomography (CBCT) finds diverse applications in medicine. Ensuring high image quality in CBCT scans is essential for accurate diagnosis and treatment delivery. Yet, the susceptibility of CBCT images to noise and artifacts ...

Esophageal varices (EV), a serious health concern resulting from portal hypertension, are traditionally diagnosed through invasive endoscopic procedures. Despite non-contrast computed tomography (NC-CT) imaging being a less expensive and non-...

Pancreatic ductal adenocarcinoma (PDAC) is infrequent. Currently, non-invasive biomarkers for early detection of PDAC are not accessible. Here, we intended to identify a set of urine markers able to discriminate patients with ...

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• The LYVE1, REG1A, and TFF1 genes in urine samples can discriminate early-stage PDAC from healthy individuals.
• The REG1A gene had a sensitivity and specificity of 100 % and 82 %, respectively, for detecting PDAC.
• The LYVE1 gene had ...

• The ability of two spectra to detect wheat powdery mildew (WPM) was compared.
• The performance of multi-feature detection WPM was compared.
• A three-band texture index could detect WPM in advance compared to other features.
• WPM ...

Remote sensing based on unmanned aerial vehicle (UAV) is a non-destructive way for wheat powdery mildew (WPM) detection in the field management and crop protection. However, WPM causes complex symptoms and impacts on wheat plants, such as ...

Lymph node (LN) assessment is a critical yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for cancer diagnosis, staging and treatment planning. Finding scatteredly distributed, ...$\mathrm{}\mathrm{}$$$

Lung cancer is the primary cause of mortality globally in both males and females, underscoring the urgent requirement for rapid and accurate methods of early detection for this condition. Computer-aided diagnosis systems have demonstrated their ...

• The effect of broken kernel content on pore structure parameters was studied.
• The porosity and permeability in the grain pile decrease with increase in the broken kernel content.
• The small pores (diameter < 1 mm) and isolated pores ...

The airflow in grain piles is affected by the content of broken grains, which causes changes in the pore structure within the grain pile. To investigate the changes in structural parameters at different broken grain contents, CT scanning and ...

In this research, the 3D volumetric segmentation of heterogeneous brain tumors such as Gliomas- anaplastic astrocytoma, and Glioblastoma Multiforme (GBM) is performed to extract enhancing tumor (ET), whole tumor (WT), and tumor core (TC) regions ...

• MS-SegNet is proposed for 3D segmentation of heterogeneous brain tumors- Gliomas
• Multi-Scale Feature Extraction (MS-FE) module extracts low and high level feature.
• MS-SegNet performs better in terms of Dice Score Coefficient, ...

Artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML) have revolutionized disease diagnosis using complex medical images such as X-rays and CT scans, significantly improving accuracy in identifying various ...

• Developed a highly accurate COVID-19 diagnosis model using transfer learning from DenseNet-121, which achieved an impressive accuracy of 95.85 % for normal and COVID-19 classes.
• Uncovered and demonstrated the vulnerability of deep ...

Hepatocellular carcinoma is a common disease with high mortality. Through deep learning methods to analyze HCC CT, the screening classification and prognosis model of HCC can be established, which further promotes the ...

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• The Skip-Layer Channel UnSampling Excitation (SCSE) module is utilized to precisely extract liver tumor features by mapping high-level features to low-level representations.
• The Adaptive Efficient Group Attention (AEG) focuses on the ...

• Accurate machine learning models minimize delays and losses in manufacturing.
• Cryogenically treated electrodes boost EDM wear prediction accuracy.
• Identified influential factors that optimize EDM processes.
• Enhancing ...

In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate ...

Osteoporosis is a metabolic disease causing structural degradation and increased fragility of bone. In particular, this affects the risk of vertebral fractures. Existing clinical methods for fracture risk assessment have been observed to have low ...

The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM) used for the delay estimation revolve around the mathematical hypothesis and assumptions, making them static with limitations in accommodating the dynamic ...

In the biomedical imaging domain, large preprocessed samples of training annotated images are required in techniques employing neural networks for effective training, which makes the method challenging and costly. Data augmentation is widely used ...

Aortic dissection (AD) is a serious condition requiring rapid and accurate diagnosis. In this study, we aimed to improve the diagnostic accuracy of AD by presenting a novel method for aortic segmentation in computed ...

• We address precise Aortic Dissection (AD) segmentation in CT with a novel model ZOZI-seg for effective diagnosis and treatment.
• ZOZI-seg uses two-stage cascade method combining CNN and Transformer to capture global context and local ...

Intracranial aneurysm is a common life-threatening disease, and the rupture of an intracranial aneurysm carries a high risk of morbidity and mortality. Due to their small size in images, it remains a challenging task to accurately extract the ...

Early detection is crucial for lung cancer to prolong the patient’s survival. Existing model architectures used in such systems have shown promising results. However, they lack reliability and robustness in their predictions and the models are ...

• Addressed the significance of uncertainty especially in dealing with medical data.
• Proposed an uncertainty-aware framework for nodule classification using 3D CT images.
• Applied three uncertainty quantification methods; Monte Carlo ...