AIMC Topic: Image Processing, Computer-Assisted

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IFT-Net: Interactive Fusion Transformer Network for Quantitative Analysis of Pediatric Echocardiography.

Medical image analysis
The task of automatic segmentation and measurement of key anatomical structures in echocardiography is critical for subsequent extraction of clinical parameters. However, the influence of boundary blur, speckle noise, and other factors increase the d...

Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection.

Abdominal radiology (New York)
PURPOSE: Fat-suppressed T2-weighted imaging (T2-FS) requires a long scan time and can be wrought with motion artifacts, urging the development of a shorter and more motion robust sequence. We compare the image quality of a single-shot T2-weighted MRI...

An uncertainty-aware deep learning architecture with outlier mitigation for prostate gland segmentation in radiotherapy treatment planning.

Medical physics
PURPOSE: Task automation is essential for efficient and consistent image segmentation in radiation oncology. We report on a deep learning architecture, comprising a U-Net and a variational autoencoder (VAE) for automatic contouring of the prostate gl...

DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT.

Medical physics
PURPOSE: Myocardial perfusion imaging (MPI) using single-photon emission-computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. In clinical practice, the long scanning procedures and acquisition time might induce ...

Radiomics in neuro-oncological clinical trials.

The Lancet. Digital health
The development of clinical trials has led to substantial improvements in the prevention and treatment of many diseases, including brain cancer. Advances in medicine, such as improved surgical techniques, the development of new drugs and devices, the...

Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet.

Medical & biological engineering & computing
Accurate lung tumor segmentation has great significance in the treatment planning of lung cancer. However, robust lung tumor segmentation becomes challenging due to the heterogeneity of tumors and the similar visual characteristics between tumors and...

A Smart Alcoholmeter Sensor Based on Deep Learning Visual Perception.

Sensors (Basel, Switzerland)
Process automation, in general, enables the enhancement of productivity, product quality, and consistency alongside other production metrics. Liquor production on an industrial scale also follows the automation trend. However, small and medium produc...

A Novel Reconstruction Algorithm with High Performance for Compressed Ultrafast Imaging.

Sensors (Basel, Switzerland)
Compressed ultrafast photography (CUP) is a type of two-dimensional (2D) imaging technique to observe ultrafast processes. Intelligence reconstruction methods that influence the imaging quality are an essential part of a CUP system. However, existing...

FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis.

Physics in medicine and biology
Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from 'hematoxylin and eosin' (HE) stained 'whole slide images' (WSIs) remains a challenge due to noise-induced intensity variation...