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End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Automatic segmentation of abdomen organs using medical imaging has many ...
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CNN Cascades for Segmenting Whole Slide Images of the Kidney
Due to the increasing availability of whole slide scanners facilitating ...
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A Comparison of Deep Learning Convolution Neural Networks for Liver Segmentation in Radial Turbo Spin Echo Images
Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can prov...
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Efficient Convolutional Neural Network with Binary Quantization Layer
In this paper we introduce a novel method for segmentation that can bene...
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Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
Cardiovascular disease accounts for 1 in every 4 deaths in United States...
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A Contrast Synthesized Thalamic Nuclei Segmentation Scheme using Convolutional Neural Networks
Thalamic nuclei have been implicated in several neurological diseases. W...
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Multispecies fruit flower detection using a refined semantic segmentation network
In fruit production, critical crop management decisions are guided by bl...
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Segmentation of histological images and fibrosis identification with a convolutional neural network
Segmentation of histological images is one of the most crucial tasks for many biomedical analyses including quantification of certain tissue type. However, challenges are posed by high variability and complexity of structural features in such images, in addition to imaging artifacts. Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations. An accurate and robust automated segmentation method is of high interest. We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain. The network comprises of 11 successive convolutional - rectified linear unit - batch normalization layers, and outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947. With 100 times fewer (only 300 thousand) trainable parameters, our CNN is less susceptible to overfitting, and is efficient. Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly. To the best of our knowledge, this is the first deep CNN tailored for the problem of concern, and may be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment.
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