AIMC Topic: Image Processing, Computer-Assisted

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Automatic improvement of deep learning-based cell segmentation in time-lapse microscopy by neural architecture search.

Bioinformatics (Oxford, England)
MOTIVATION: Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and complicated temporal behaviors. In recent yea...

Reducing speckle in anterior segment optical coherence tomography images based on a convolutional neural network.

Applied optics
Speckle noise is ubiquitous in the optical coherence tomography (OCT) image of the anterior segment, which greatly affects the image quality and destroys the relevant structural information. In order to reduce the influence of speckle noise in OCT im...

Fully unsupervised deep mode of action learning for phenotyping high-content cellular images.

Bioinformatics (Oxford, England)
MOTIVATION: The identification and discovery of phenotypes from high content screening images is a challenging task. Earlier works use image analysis pipelines to extract biological features, supervised training methods or generate features with neur...

Augmenting Osteoporosis Imaging with Machine Learning.

Current osteoporosis reports
PURPOSE OF REVIEW: In this paper, we discuss how recent advancements in image processing and machine learning (ML) are shaping a new and exciting era for the osteoporosis imaging field. With this paper, we want to give the reader a basic exposure to ...

Phase2Phase: Respiratory Motion-Resolved Reconstruction of Free-Breathing Magnetic Resonance Imaging Using Deep Learning Without a Ground Truth for Improved Liver Imaging.

Investigative radiology
OBJECTIVES: Respiratory binning of free-breathing magnetic resonance imaging data reduces motion blurring; however, it exacerbates noise and introduces severe artifacts due to undersampling. Deep neural networks can remove artifacts and noise but usu...

Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.

Blood
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times e...

Automatic segmentation of paravertebral muscles in abdominal CT scan by U-Net: The application of data augmentation technique to increase the Jaccard ratio of deep learning.

Medicine
Sarcopenia, characterized by a decline of skeletal muscle mass, has emerged as an important prognostic factor for cancer patients. Trunk computed tomography (CT) is a commonly used modality for assessment of cancer disease extent and treatment outcom...

CellProfiler Analyst 3.0: accessible data exploration and machine learning for image analysis.

Bioinformatics (Oxford, England)
SUMMARY: Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of...

Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints.

Bioinformatics (Oxford, England)
MOTIVATION: In most tissue-based biomedical research, the lack of sufficient pathology training images with well-annotated ground truth inevitably limits the performance of deep learning systems. In this study, we propose a convolutional neural netwo...

OCTID: a one-class learning-based Python package for tumor image detection.

Bioinformatics (Oxford, England)
MOTIVATION: Tumor tile selection is a necessary prerequisite in patch-based cancer whole slide image analysis, which is labor-intensive and requires expertise. Whole slides are annotated as tumor or tumor free, but tiles within a tumor slide are not....