AIMC Topic: Image Processing, Computer-Assisted

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MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability.

AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit's activation via adjoint operators.

Neural networks : the official journal of the International Neural Network Society
Adjoint operators have been found to be effective in the exploration of CNN's inner workings (Wan and Choe, 2022). However, the previous no-bias assumption restricted its generalization. We overcome the restriction via embedding input images into an ...

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.

Scientific reports
Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features...

Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.

European radiology experimental
BACKGROUND: Chest x-ray is commonly used for pulmonary abnormality screening. However, since the image characteristics of x-rays highly depend on the machine specifications, an artificial intelligence (AI) model developed for specific equipment usual...

Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol.

Deep learning-based 3D brain multimodal medical image registration.

Medical & biological engineering & computing
Medical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this pape...

A combined encoder-transformer-decoder network for volumetric segmentation of adrenal tumors.

Biomedical engineering online
BACKGROUND: The morphology of the adrenal tumor and the clinical statistics of the adrenal tumor area are two crucial diagnostic and differential diagnostic features, indicating precise tumor segmentation is essential. Therefore, we build a CT image ...

Foreground segmentation network using transposed convolutional neural networks and up sampling for multiscale feature encoding.

Neural networks : the official journal of the International Neural Network Society
Foreground segmentation algorithm aims to precisely separate moving objects from the background in various environments. However, the interference from darkness, dynamic background information, and camera jitter makes it still challenging to build a ...

Prompt tuning for parameter-efficient medical image segmentation.

Medical image analysis
Neural networks pre-trained on a self-supervision scheme have become the standard when operating in data rich environments with scarce annotations. As such, fine-tuning a model to a downstream task in a parameter-efficient but effective way, e.g. for...