IEEE transactions on medical imaging
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IEEE Trans Med Imaging · May 2017
3-D Morphology Prediction of Progressive Spinal Deformities From Probabilistic Modeling of Discriminant Manifolds.
We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3-D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning has allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the data. We propose a discriminative probabilistic manifold embedding where locally linear mappings transform data points from high-dimensional space to corresponding low-dimensional coordinates. ⋯ Rate of progression is modulated from the spine flexibility and curve magnitude of the 3-D spine deformation. The method was tested on 745 reconstructions from 133 subjects using longitudinal 3-D reconstructions of the spine, with results demonstrating the discriminatory framework can identify between P and NP of scoliotic patients with a classification rate of 81% and the prediction differences of 2.1° in main curve angulation, outperforming other manifold learning methods. Our method achieved a higher prediction accuracy and improved the modeling of spatiotemporal morphological changes in highly deformed spines compared with other learning methods.
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IEEE Trans Med Imaging · Oct 2016
Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI.
Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b -values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. ⋯ In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b -values (3000 s ·mm-2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.
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In the recent MRI scanning, ultra-high-field (7T) MR imaging provides higher resolution and better tissue contrast compared to routine 3T MRI, which may help in more accurate and early brain diseases diagnosis. However, currently, 7T MRI scanners are more expensive and less available at clinical and research centers. These motivate us to propose a method for the reconstruction of images close to the quality of 7T MRI, called 7T-like images, from 3T MRI, to improve the quality in terms of resolution and contrast. ⋯ The results show that our method outperforms previous methods and is able to recover better structural details. Also, to place our proposed method in a medical application context, we evaluated the influence of post-processing methods such as brain tissue segmentation on the reconstructed 7T-like MR images. Results show that our 7T-like images lead to higher accuracy in segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and skull, compared to segmentation of 3T MR images.
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IEEE Trans Med Imaging · Jun 2016
Computer-Assisted Screw Size and Insertion Trajectory Planning for Pedicle Screw Placement Surgery.
Pathological conditions that cause instability of the spine are commonly treated by vertebral fixation involving pedicle screw placement surgery. However, existing methods for preoperative planning are based only on geometrical properties of vertebral structures (i.e., shape) without taking into account their structural properties (i.e., appearance). We propose a novel automated method for computer-assisted preoperative planning of the thoracic pedicle screw size and insertion trajectory. ⋯ In terms of mean absolute difference (MAD) and corresponding standard deviation (SD), the resulting high modeling accuracy of 0.39±0.31 mm for 3D vertebral body models and 0.31±0.25 mm for 3D pedicle models created an adequate anatomical frame for 3D pedicle screw models. When comparing the automatically obtained and manually defined plans for pedicle screw placement, a relatively high agreement was observed, with MAD ±SD of 0.4±0.4 mm for the screw diameter, 5.8±4.2 mm for the screw length, 2.0±1.4 mm for the pedicle crossing point and 7.6±5.8(°) for screw insertion angles. However, a statistically significant increase of 48±26% in the screw fastening strength in favor of the proposed automated method was observed in 99% of the cases.
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IEEE Trans Med Imaging · May 2016
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. ⋯ In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.