Lung cancer is the deadliest cancer worldwide. Early detection of lung cancer is a promising way to lower the risk of dying. Accurate pulmonary nodule detection in computed tomography (CT) images is crucial for early diagnosis of lung cancer. The development of computer-aided detection (CAD) system of pulmonary nodules contributes to making the CT analysis more accurate and with more efficiency. Recent studies from other groups have been focusing on lung cancer diagnosis CAD system by detecting medium to large nodules. However, to fully investigate the relevance between nodule features and cancer diagnosis, a CAD that is capable of detecting nodules with all sizes is needed. In this paper, we present a deep-learning based automatic all size pulmonary nodule detection system by cascading two artificial neural networks. We firstly use a U-net like 3D network to generate nodule candidates from CT images. Then, we use another 3D neural network to refine the locations of the nodule candidates generated from the previous subsystem. With the second sub-system, we bring the nodule candidates closer to the center of the ground truth nodule locations. We evaluate our system on a public CT dataset provided by the Lung Nodule Analysis (LUNA) 2016 grand challenge. The performance on the testing dataset shows that our system achieves 90% sensitivity with an average of 4 false positives per scan. This indicates that our system can be an aid for automatic nodule detection, which is beneficial for lung cancer diagnosis.
Accurate lumbar spine measurement in CT images provides an essential way for quantitative spinal diseases analysis such as spondylolisthesis and scoliosis. In today’s clinical workflow, the measurements are manually performed by radiologists and surgeons, which is time consuming and irreproducible. Therefore, automatic and accurate lumbar spine measurement algorithm becomes highly desirable. In this study, we propose a method to automatically calculate five different lumbar spine measurements in CT images. There are three main stages of the proposed method: First, a learning based spine labeling method, which integrates both the image appearance and spine geometry information, is used to detect lumbar and sacrum vertebrae in CT images. Then, a multiatlases based image segmentation method is used to segment each lumbar vertebra and the sacrum based on the detection result. Finally, measurements are derived from the segmentation result of each vertebra. Our method has been evaluated on 138 spinal CT scans to automatically calculate five widely used clinical spine measurements. Experimental results show that our method can achieve more than 90% success rates across all the measurements. Our method also significantly improves the measurement efficiency compared to manual measurements. Besides benefiting the routine clinical diagnosis of spinal diseases, our method also enables the large scale data analytics for scientific and clinical researches.
Automatically detecting anatomy orientation is an important task in medical image analysis. Specifically, the ability to automatically detect coarse orientation of structures is useful to minimize the effort of fine/accurate orientation detection algorithms, to initialize non-rigid deformable registration algorithms or to align models to target structures in model-based segmentation algorithms. In this work, we present a deep convolution neural network (DCNN)-based method for fast and robust detection of the coarse structure orientation, i.e., the hemi-sphere where the principal axis of a structure lies. That is, our algorithm predicts whether the principal orientation of a structure is in the northern hemisphere or southern hemisphere, which we will refer to as UP and DOWN, respectively, in the remainder of this manuscript. The only assumption of our method is that the entire structure is located within the scan’s field-of-view (FOV). To efficiently solve the problem in 3D space, we formulated it as a multi-planar 2D deep learning problem. In the training stage, a large number coronal-sagittal slice pairs are constructed as 2-channel images to train a DCNN to classify whether a scan is UP or DOWN. During testing, we randomly sample a small number of coronal-sagittal 2-channel images and pass them through our trained network. Finally, coarse structure orientation is determined using majority voting. We tested our method on 114 Elbow MR Scans. Experimental results suggest that only five 2-channel images are sufficient to achieve a high success rate of 97.39%. Our method is also extremely fast and takes approximately 50 milliseconds per 3D MR scan. Our method is insensitive to the location of the structure in the FOV.
Automatic and precise segmentation of hand bones is important for many medical imaging applications. Although several previous studies address bone segmentation, automatically segmenting articulated hand bones remains a challenging task. The highly articulated nature of hand bones limits the effectiveness of atlas-based segmentation methods. The use of low-level information derived from the image-of-interest alone is insufficient for detecting bones and distinguishing boundaries of different bones that are in close proximity to each other. In this study, we propose a method that combines an articulated statistical shape model and a local exemplar-based appearance model for automatically segmenting hand bones in CT. Our approach is to perform a hierarchical articulated shape deformation that is driven by a set of local exemplar-based appearance models. Specifically, for each point in the shape model, the local appearance model is described by a set of profiles of low-level image features along the normal of the shape. During segmentation, each point in the shape model is deformed to a new point whose image features are closest to the appearance model. The shape model is also constrained by an articulation model described by a set of pre-determined landmarks on the finger joints. In this way, the deformation is robust to sporadic false bony edges and is able to fit fingers with large articulations. We validated our method on 23 CT scans and we have a segmentation success rate of ~89.70 %. This result indicates that our method is viable for automatic segmentation of articulated hand bones in conventional CT.
In X-ray examinations, it is essential that radiographers carefully use collimation to the appropriate anatomy of interest to minimize the overall integral dose to the patient. The shadow regions are not diagnostically meaningful and could impair the overall image quality. Thus, it is desirable to detect the collimation and exclude the shadow regions to optimize image display. However, due to the large variability of collimated images, collimation detection remains a challenging task. In this paper, we consider a region of interest (ROI) in an image, such as the collimation, can be described by two distinct views, a cluster of pixels within the ROI and the corners of the ROI. Based on this observation, we propose a robust multi-view learning based strategy for collimation detection in digital radiography. Specifically, one view is from random forests learning based region detector, which provides pixel-wise image classification and each pixel is labeled as either in-collimation or out-of-collimation. The other view is from a discriminative, learning-based landmark detector, which detects the corners and localizes the collimation within the image. Nevertheless, given the huge variability of the collimated images, the detection from either view alone may not be perfect. Therefore, we adopt an adaptive view fusing step to obtain the final detection by combining region and corner detection. We evaluate our algorithm in a database with 665 X-ray images in a wide variety of types and dosages and obtain a high detection accuracy (95%), compared with using region detector alone (87%) and landmark detector alone (83%).
Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.
Purpose: By incorporating high-level shape priors, atlas-based segmentation has achieved tremendous success
in the area of medical image analysis. However, the effect of various kinds of atlases, e.g., average shape model,
example-based multi-atlas, has not been fully explored. In this study, we aim to generate different atlases and
compare their performance in segmentation.
Methods: We compare segmentation performance using parametric deformable model with four different atlases,
including 1) a single atlas, i.e., average shape model (SAS); 2) example-based multi-atlas (EMA); 3) cluster-based
average shape models (CAS); 4) cluster-based statistical shape models (average shape + principal shape variation
modes)(CSS). CAS and CSS are novel atlases constructed by shape clustering. For comparison purpose, we also
use PDM without atlas (NOA) as a benchmark method.
Experiments: The experiment is carried on liver segmentation from whole-body CT images. Atlases are
constructed by 39 manually delineated liver surfaces. 11 CT scans with ground truth are used as testing data
set. Segmentation accuracy using different atlases are compared.
Conclusion: Compared with segmentation without atlas, all of the four atlas-based image segmentation methods
achieve better results. Multi-atlas based segmentation behaves better than single-atlas based segmentation. CAS
exhibit superior performance to all other methods.
KEYWORDS: Detection and tracking algorithms, Medical imaging, Brain, Spine, Magnetic resonance imaging, 3D applications, Neuroimaging, Data analysis, 3D image processing, Imaging systems
One of primary challenges in the medical image data analysis is the ability to handle abnormal, irregular and/or
partial cases. In this paper, we present two different robust algorithms towards the goal of automatic planar
primitive detection in 3D volumes. The overall algorithm is a bottoms-up approach starting with the anatomic
point primitives (or landmarks) detection. The robustness in computing the planar primitives is built in through
both a novel consensus-based voting approach, and a random sampling-based weighted least squares regression
method. Both these approaches remove inconsistent landmarks and outliers detected in the landmark detection
step. Unlike earlier approaches focused towards a particular plane, the presented approach is generic and can be
easily adapted to computing more complex primitives such as ROIs or surfaces. To demonstrate the robustness
and accuracy of our approach, we present extensive results for automatic plane detection (Mig-Sagittal and
Optical Triangle planes) in brain MR-images. In comparison to ground truth, our approach has marginal errors
on about 90 patients. The algorithm also works really well under adverse conditions of arbitrary rotation and
cropping of the 3D volume. In order to exhibit generalization of the approach, we also present preliminary results
on intervertebrae-plane detection for 3D spine MR application.
We present an automatic method to quickly and accurately detect multiple anatomy region-of-interests (ROIs) from CT
topogram images. Our method first detects a redundant and potentially erroneous set of local features. Their spatial
configurations are captured by a set of local voting functions. Unlike all the existing methods where the idea was to try to
"hit" the correct/best constellations of local features, we have taken an opposite approach. We try to peel away the bad
features until a safe (i.e., conservatively small) number of features remain. It is deterministic in nature and guarantees
a success even for extremely noisy cases. The advantages of the method are its robustness and computational efficiency.
Our method also addresses the potential scenario in which outliers (i.e., false landmarks detections) forms plausible
configurations. As long as such outliers are a minority, the method can successfully remove these outliers. The final ROI
of the anatomy is computed from a best subset of the remaining local features. Experimental validation was carried out
for multiple organs detection from a large collection of CT topogram images. Fast and highly robust performance was
observed. In the testing data sets, the detection rate varies from 98.2% to 100% for different ROIs and the false detection
rate is from 0.0% to 0.5% for different ROIs. The method is fast and accurate enough to be seamlessly integrated into a
real-time work flow on the CT machine to improve efficiency, consistency, and repeatability.
Emerging whole-body imaging technologies push computer aided detection/diagnosis (CAD) to scale up to a
whole-body level, which involves multiple organs or anatomical structure. To be exploited in this paper is the
fact that the various tasks in whole-body CAD are often highly dependent (e.g., the localization of the femur
heads strongly predicts the position of the iliac bifurcation of the aorta). One way to effectively employ task
dependency is to schedule the tasks such that outputs of some tasks are used to guide the others. In this sense,
optimal task scheduling is key to improve overall performance of a whole-body CAD system. In this paper,
we propose a method for task scheduling that is optimal in an information-theoretic sense. The central idea
is to schedule tasks in such an order that each operation achieves maximum expected information gain over
all the tasks. The formulation embeds two intuitive principles: (1) a task with higher confidence tends to be
scheduled earlier; (2) a task with higher predictive power for other tasks tends to be scheduled earlier. More
specifically, task dependency is modeled by conditional probability; the outcome of each task is assumed to be
probabilistic as well; and the objective function is based on the reduction of the summed conditional entropy
over all tasks. The validation is carried out on a challenging CAD problem, multi-organ localization in whole-body
CT. Compared to unscheduled and ad hoc scheduled organ detection/localization, our scheduled execution
achieves higher accuracy with much less computation time.
Reliable landmark detection in medical images provides the essential groundwork for successful automation of
various open problems such as localization, segmentation, and registration of anatomical structures. In this paper,
we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical
landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the
advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark
detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging
modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different
phases of the detection stage combined with robust features that are highly efficient in terms of computation
time enables a seemingly real time performance, with very high localization accuracy.
This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and
abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a
run time efficiency in landmark detection. It also shows good scalability performance under increasing number
of landmarks.
To automatically segment moving objects in video sequences, FUB and ETRI have proposed several approaches to combine results provided by temporal segmentation methods (by FUB and UH) and spatial segmentation methods (by ETRI). In this paper, the authors present a novel approach that fuses temporal and spatial information during the process of segmentation rather than combine temporal and spatial segmentation results. The proposed approach is based on a region binding process, during which temporal segmentation results are integrated. That regions are distributedly represented and characterized distinguishes the region binding from the region merging and region growing. By fusing both temporal and spatial information, primitively segmented regions are bound to form the Binding-Cores (BC), whose role is similar to that of seeds in the region growing. Then the rest regions are bound to their neighboring BCs under strong or weak rules. The approach is composed of four stages. The experiment results show the performance of the approach.
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