Automatic Spine Curvature Estimation from X-ray Images of a Mouse Model

Automatic segmentation and quantification of skeletal structures has a variety of applications for biological research. Although solutions for good quality X-ray images of human skeletal structures are in existence in recent years, automatic solutions working on poor quality X-ray images of mice are rare. This paper proposes a fully automatic solution for spine segmentation and curvature quantification from X-ray images of mice. The proposed solution consists of three stages, namely preparation of the region of interest, spine segmentation, and spine curvature quantification, aiming to overcome technical difficulties in processing the X-ray images. We examined six different automatic measurements for quantifying the spine curvature through tests on a sample data set of 100 images. The experimental results show that some of the automatic measures are very close to and consistent with the best manual measurement results by annotators. The test results also demonstrate the effectiveness of the curvature quantification produced by the proposed solution in distinguishing abnormally shaped spines from the normal ones with accuracy up to 98.6%

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge "Accurate Automated Spinal Curvature Estimation, MICCAI 2019" (100 scans). On the challenge's test set, we obtained an average symmetric mean absolute percentage error of 22.96

Automatic pelvis segmentation from x-ray images of a mouse model

The automatic detection and quantification of skeletal structures has a variety of different applications for biological research. Accurate segmentation of the pelvis from X-ray images of mice in a high-throughput project such as the Mouse Genomes Project not only saves time and cost but also helps achieving an unbiased quantitative analysis within the phenotyping pipeline. This paper proposes an automatic solution for pelvis segmentation based on structural and orientation properties of the pelvis in X-ray images. The solution consists of three stages including pre-processing image to extract pelvis area, initial pelvis mask preparation and final pelvis segmentation. Experimental results on a set of 100 X-ray images showed consistent performance of the algorithm. The automated solution overcomes the weaknesses of a manual annotation procedure where intra- and inter-observer variations cannot be avoided

Fast and Robust Automatic Segmentation Methods for MR Images of Injured and Cancerous Tissues

Magnetic Resonance Imaging: MRI) is a key medical imaging technology. Through in vivo soft tissue imaging, MRI allows clinicians and researchers to make diagnoses and evaluations that were previously possible only through biopsy or autopsy. However, analysis of MR images by domain experts can be time-consuming, complex, and subject to bias. The development of automatic segmentation techniques that make use of robust statistical methods allows for fast and unbiased analysis of MR images. In this dissertation, I propose segmentation methods that fall into two classes---(a) segmentation via optimization of a parametric boundary, and: b) segmentation via multistep, spatially constrained intensity classification. These two approaches are applicable in different segmentation scenarios. Parametric boundary segmentation is useful and necessary for segmentation of noisy images where the tissue of interest has predictable shape but poor boundary delineation, as in the case of lung with heavy or diffuse tumor. Spatially constrained intensity classification is appropriate for segmentation of noisy images with moderate contrast between tissue regions, where the areas of interest have unpredictable shapes, as is the case in spinal injury and brain tumor. The proposed automated segmentation techniques address the need for MR image analysis in three specific applications:: 1) preclinical rodent studies of primary and metastatic lung cancer: approach: a)),: 2) preclinical rodent studies of spinal cord lesion: approach: b)), and: 3) postclinical analysis of human brain cancer: approach: b)). In preclinical rodent studies of primary and metastatic lung cancer, respiratory-gated MRI is used to quantitatively measure lung-tumor burden and monitor the time-course progression of individual tumors. I validate a method for measuring tumor burden based upon average lung-image intensity. The method requires accurate lung segmentation; toward this end, I propose an automated lung segmentation method that works for varying tumor burden levels. The method includes development of a novel, two-dimensional parametric model of the mouse lungs and a multifaceted cost function to optimally fit the model parameters to each image. Results demonstrate a strong correlation: 0.93), comparable with that of fully manual expert segmentation, between the automated method\u27s tumor-burden metric and the tumor burden measured by lung weight. In preclinical rodent studies of spinal cord lesion, MRI is used to quantify tissues in control and injured mouse spinal cords. For this application, I propose a novel, multistep, multidimensional approach, utilizing the Classification Expectation Maximization: CEM) algorithm, for automatic segmentation of spinal cord tissues. In contrast to previous methods, my proposed method incorporates prior knowledge of cord geometry and the distinct information contained in the different MR images gathered. Unlike previous approaches, the algorithm is shown to remain accurate for whole spinal cord, white matter, and hemorrhage segmentation, even in the presence of significant injury. The results of the method are shown to be on par with expert manual segmentation. In postclinical analysis of human brain cancer, access to large collections of MRI data enables scientifically rigorous study of cancers like glioblastoma multiforme, the most common form of malignant primary brain tumor. For this application, I propose an efficient and effective automated segmentation method, the Enhanced Classification Expectation Maximization: ECEM) algorithm. The ECEM algorithm is novel in that it introduces spatial information directly into the classical CEM algorithm, which is otherwise spatially unaware, with low additional computational complexity. I compare the ECEM\u27s performance on simulated data to the standard finite Gaussian mixture EM algorithm, which is not spatially aware, and to the hidden-Markov random field EM algorithm, a commonly-used spatially aware automated segmentation method for MR brain images. I also show sample results demonstrating the ECEM algorithm\u27s ability to segment MR images of glioblastoma

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

Objective. To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment. Methods. A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs. Results. For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°. Conclusion. The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN. Significance. Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

X-ray Image Segmentation and An Internet-based Tool for Medical Validation

Segmentation of vertebrae in X-ray images is a difficult task that requires an effective segmentation procedure. Noise, poor image contrast, occlusions and shape variability are some of the challenges in many of the spine X-ray images archived at the U.S. National Library of Medicine (NLM). In this thesis, we propose a curvature-based corner matching approach, which exploits the posterior corners of the vertebra to estimate the location and orientation of the vertebrae. The key advantage of the proposed approach is execution time, roughly about one-fifth of the previous approach that uses the generalized Hough transform when tested on a sizeable set of cervical spine images. This thesis also presents the first ever effort to develop a prototype internet-based medical image segmentation and pathology validation tool, which enables radiologists to validate computer generated image segmentations, modify existing or create new segmentation in addition to identifying pertinent pathology data