AIMC Topic: Image Processing, Computer-Assisted

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Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction.

IEEE transactions on medical imaging
Accelerating the data acquisition of dynamic magnetic resonance imaging leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning communities over the last decades. The ke...

Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks.

Journal of biophotonics
Convolutional neural networks (CNNs) are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired b...

A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning.

NeuroImage. Clinical
BACKGROUND AND PURPOSE: With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided dete...

Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning.

PLoS computational biology
The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. ...

Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?

International journal of neural systems
Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging moda...

Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks.

Annals of biomedical engineering
The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Automatic image processing techniques that allow for high-throughput and precise discrimination of tumor ...

Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning.

IEEE transactions on medical imaging
Many medical image segmentation methods are based on the supervised classification of voxels. Such methods generally perform well when provided with a training set that is representative of the test images to the segment. However, problems may arise ...

Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach.

IEEE transactions on pattern analysis and machine intelligence
Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a databas...

Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis.

IEEE transactions on medical imaging
We focus on the practical challenge of segmenting new retinal fundus images that are dissimilar to existing well-annotated data sets. It is addressed in this paper by a supervised learning pipeline, with its core being the construction of a synthetic...

Learning to Segment Object Candidates via Recursive Neural Networks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images. In this paper, we present a simple yet effec...