Computer algorithms for three-dimensional measurement of humeral anatomy: analysis of 140 paired humeri.

BACKGROUND: In the presence of severe osteoarthritis, osteonecrosis, or proximal humeral fracture, the contralateral humerus may serve as a template for the 3-dimensional (3D) preoperative planning of reconstructive surgery. The purpose of this study was to develop algorithms for performing 3D measurements of the humeral anatomy and further to assess side-to-side (bilateral) differences in humeral head retrotorsion, humeral head inclination, humeral length, and humeral head radius and height. METHODS: The 3D models of 140 paired humeri (70 cadavers) were extracted from computed tomographic data. Geometric characteristics quantifying the humeral anatomy in 3D were determined in a semiautomatic fashion using the developed computer algorithms. The results between the sides were compared for evaluating bilateral differences. RESULTS: The mean bilateral difference of the humeral retrotorsion angle was 6.7° (standard deviation [SD], 5.7°; range, -15.1° to 24.0°; P = .063); the mean side difference of the humeral head inclination angle was 2.3° (SD, 1.8°; range, -5.1° to 8.4°; P = .12). The side difference in humeral length (mean, 2.9 mm; SD, 2.5 mm; range, -8.7 mm to 10.1 mm; P = .04) was significant. The mean side difference in the head sphere radius was 0.5 mm (SD, 0.6 mm; range, -3.2 mm to 2.2 mm; P = .76), and the mean side difference in humeral head height was 0.8 mm (SD, 0.6 mm; range, -2.4 mm to 2.4 mm; P = .44). CONCLUSIONS: The contralateral anatomy may serve as a reliable reconstruction template for humeral length, humeral head radius, and humeral head height if it is analyzed with 3D algorithms. In contrast, determining humeral head retrotorsion and humeral head inclination from the contralateral anatomy may be more prone to error.

Matricellular Protein SPARCL1 Regulates Blood Vessel Integrity and Antagonizes Inflammatory Bowel Disease.

BACKGROUND: The understanding of vascular plasticity is key to defining the role of blood vessels in physiologic and pathogenic processes. In the present study, the impact of the vascular quiescence marker SPARCL1 on angiogenesis, capillary morphogenesis, and vessel integrity was evaluated. METHODS: Angiogenesis was studied using the metatarsal test, an ex vivo model of sprouting angiogenesis. In addition, acute and chronic dextran sodium sulfate colitis models with SPARCL1 knockout mice were applied. RESULTS: This approach indicated that SPARCL1 inhibits angiogenesis and supports vessel morphogenesis and integrity. Evidence was provided that SPARCL1-mediated stabilization of vessel integrity counteracts vessel permeability and inflammation in acute and chronic dextran sodium sulfate colitis models. Structure-function analyses of purified SPARCL1 identified the acidic domain of the protein necessary for its anti-angiogenic activity. CONCLUSIONS: Our findings inaugurate SPARCL1 as a blood vessel-derived anti-angiogenic molecule required for vessel morphogenesis and integrity. SPARCL1 opens new perspectives as a vascular marker of susceptibility to colitis and as a therapeutic molecule to support blood vessel stability in this disease.

Evaluation of deep learning methods for parotid gland segmentation from CT images.

The segmentation of organs at risk is a crucial and time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and low contrast to surrounding structures, segmenting the parotid gland is challenging. Motivated by the recent success of deep learning, we study the use of two-dimensional (2-D), 2-D ensemble, and three-dimensional (3-D) U-Nets for segmentation. The mean Dice similarity to ground truth is ∼ 0.83 for all three models. A patch-based approach for class balancing seems promising for false-positive reduction. The 2-D ensemble and 3-D U-Net are applied to the test data of the 2015 MICCAI challenge on head and neck autosegmentation. Both deep learning methods generalize well onto independent data (Dice 0.865 and 0.88) and are superior to a selection of model- and atlas-based methods with respect to the Dice coefficient. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed for training. We evaluate the performance after training with different-sized training sets and observe no significant increase in the Dice coefficient for more than 250 training cases.

Isotropic Total Variation Regularization of Displacements in Parametric Image Registration.

Spatial regularization is essential in image registration, which is an ill-posed problem. Regularization can help to avoid both physically implausible displacement fields and local minima during optimization. Tikhonov regularization (squared l2 -norm) is unable to correctly represent non-smooth displacement fields, that can, for example, occur at sliding interfaces in the thorax and abdomen in image time-series during respiration. In this paper, isotropic Total Variation (TV) regularization is used to enable accurate registration near such interfaces. We further develop the TV-regularization for parametric displacement fields and provide an efficient numerical solution scheme using the Alternating Directions Method of Multipliers (ADMM). The proposed method was successfully applied to four clinical databases which capture breathing motion, including CT lung and MR liver images. It provided accurate registration results for the whole volume. A key strength of our proposed method is that it does not depend on organ masks that are conventionally required by many algorithms to avoid errors at sliding interfaces. Furthermore, our method is robust to parameter selection, allowing the use of the same parameters for all tested databases. The average target registration error (TRE) of our method is superior (10% to 40%) to other techniques in the literature. It provides precise motion quantification and sliding detection with sub-pixel accuracy on the publicly available breathing motion databases (mean TREs of 0.95 mm for DIR 4D CT, 0.96 mm for DIR COPDgene, 0.91 mm for POPI databases).

Prediction of normal bone anatomy for the planning of corrective osteotomies of malunited forearm bones using a three-dimensional statistical shape model.

Corrective osteotomies of the forearm based on 3D computer simulation using contralateral anatomy as a reconstruction template is an approved method. Limitations are existing considerable differences between left and right forearms, and that a healthy contralateral anatomy is required. We evaluated if a computer model, not relying on the contralateral anatomy, may replace the current method by predicting the pre-traumatic healthy shape. A statistical shape model (SSM) was generated from a set of 59 CT scans of healthy forearms, encoding the normal anatomical variations. Three different configurations were simulated to predict the pre-traumatic shape with the SSM (cross-validation). In the first two, only the distal or proximal 50% of the radius were considered as pathological. In a third configuration, the entire radius was assumed to be pathological, only the ulna being intact. Corresponding experiments were performed with the ulna. Accuracy of the prediction was assessed by comparing the predicted bone with the healthy model. For the radius, mean rotation accuracy of the prediction between 2.9 ± 2.2° and 4.0 ± 3.1° in pronation/supination, 0.4 ± 0.3° and 0.6 ± 0.5° in flexion/extension, between 0.5 ± 0.3° and 0.5 ± 0.4° in radial-/ulnarduction. Mean translation accuracy along the same axes between 0.8 ± 0.7 and 1.0 ± 0.8 mm, 0.5 ± 0.4 and 0.6 ± 0.4 mm, 0.6 ± 0.4 and 0.6 ± 0.5 mm, respectively. For the ulna, mean rotation accuracy between 2.4 ± 1.9° and 4.7 ± 3.8° in pronation/supination, 0.3 ± 0.3° and 0.8 ± 0.6° in flexion/extension, 0.3 ± 0.2° and 0.7 ± 0.6° in radial-/ulnarduction. Mean translation accuracy between 0.6 ± 0.4 mm and 1.3 ± 0.9 mm, 0.4 ± 0.4 mm and 0.7 ± 0.5 mm, 0.5 ± 0.4 mm and 0.8 ± 0.6 mm, respectively. This technique provided high accuracy, and may replace the current method, if validated in clinical studies. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2630-2636, 2017.

Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.

PURPOSE: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. METHODS: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. RESULTS: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. CONCLUSIONS: The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.

Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks.

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.

Consistency-based rectification of nonrigid registrations.

We present a technique to rectify nonrigid registrations by improving their group-wise consistency, which is a widely used unsupervised measure to assess pair-wise registration quality. While pair-wise registration methods cannot guarantee any group-wise consistency, group-wise approaches typically enforce perfect consistency by registering all images to a common reference. However, errors in individual registrations to the reference then propagate, distorting the mean and accumulating in the pair-wise registrations inferred via the reference. Furthermore, the assumption that perfect correspondences exist is not always true, e.g., for interpatient registration. The proposed consistency-based registration rectification (CBRR) method addresses these issues by minimizing the group-wise inconsistency of all pair-wise registrations using a regularized least-squares algorithm. The regularization controls the adherence to the original registration, which is additionally weighted by the local postregistration similarity. This allows CBRR to adaptively improve consistency while locally preserving accurate pair-wise registrations. We show that the resulting registrations are not only more consistent, but also have lower average transformation error when compared to known transformations in simulated data. On clinical data, we show improvements of up to 50% target registration error in breathing motion estimation from four-dimensional MRI and improvements in atlas-based segmentation quality of up to 65% in terms of mean surface distance in three-dimensional (3-D) CT. Such improvement was observed consistently using different registration algorithms, dimensionality (two-dimensional/3-D), and modalities (MRI/CT).

Simultaneous segmentation and multiresolution nonrigid atlas registration.

In this paper, a novel Markov random field (MRF)-based approach is presented for segmenting medical images while simultaneously registering an atlas nonrigidly. In the literature, both segmentation and registration have been studied extensively. For applications that involve both, such as segmentation via atlas-based registration, earlier studies proposed addressing these problems iteratively by feeding the output of each to initialize the other. This scheme, however, cannot guarantee an optimal solution for the combined task at hand, since these two individual problems are then treated separately. In this paper, we formulate simultaneous registration and segmentation (SRS) as a maximum a-posteriori (MAP) problem. We decompose the resulting probabilities such that the MAP inference can be done using MRFs. An efficient hierarchical implementation is employed, allowing coarse-to-fine registration while estimating segmentation at pixel level. The method is evaluated on two clinical data sets: 1) mandibular bone segmentation in 3D CT and 2) corpus callosum segmentation in 2D midsaggital slices of brain MRI. A video tracking example is also given. Our implementation allows us to directly compare the proposed method with the individual segmentation/registration and the iterative approach using the exact same potential functions. In a leave-one-out evaluation, SRS demonstrated more accurate results in terms of dice overlap and surface distance metrics for both data sets. We also show quantitatively that the SRS method is less sensitive to the errors in the registration as opposed to the iterative approach.

Latent log-linear models for handwritten digit classification.

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.