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DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
TLDR
This paper presents a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography CT scans, using multi-level deep convolutional networks ConvNets, and proposes and evaluates several variations of deep ConvNETS in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels.
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A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations
TLDR
This work operates a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI), and decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates.
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Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
TLDR
Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets.
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A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling
TLDR
Under six-fold cross-validation, the bottom-up segmentation method significantly outperforms its MALF counterpart and the segmentation framework using deep patch labeling confidences is also more numerically stable, as reflected in the smaller performance metric standard deviations.
View on IEEE
2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers
TLDR
A new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue is proposed.
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Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest.
TLDR
Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT.
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Uldor: A Universal Lesion Detector For Ct Scans With Pseudo Masks And Hard Negative Example Mining
TLDR
This work builds a Universal Lesion Detector (ULDor) based on Mask R-CNN, which is able to detect all different kinds of lesions from whole body parts and proposes a hard negative example mining strategy to reduce the false positives for improving the detection performance.
View on IEEE
Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment.
TLDR
Overall age-related muscle density (attenuation) loss was steeper than for muscle area for both sexes, and therefore may be a more valuable predictor of adverse outcomes, according to a fully automated CT muscle tool applied to routine abdominal CT scans.
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Feasibility of Simultaneous Computed Tomographic Colonography and Fully Automated Bone Mineral Densitometry in a Single Examination
TLDR
Osteoporosis screening can be performed simultaneously with screening for colorectal polyps using fully automated software and fully automated BMD measurements were reproducible for a given patient.
View on Wolters Kluwer
Utilizing Fully Automated Abdominal CT–Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms
TLDR
This study investigates the use of fully FDA-approved drugs to treat metabolic syndrome, which describes a constellation of reversible cardiometabolic abnormalities associated with cardiovascular risk and diabetes.
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