Automatic segmentation of the left ventricle in 3D SPECT data by registration with a dynamic anatomic model

Survey of Non-Rigid Registration Tools in Medicine

We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using non-systematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support; (ii) 7 are built on software platforms, 5 were developed for brain image registration; (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016; (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools; and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively. Researchers in medical image analysis already have a large choice of registration tools freely available. However, the most recently published algorithms may not be included in the tools, yet.

A non-rigid registration method for the analysis of local deformations in the wood cell wall

This paper concerns the problem of wood cellular structure image registration. Given the large variability of wood geometry and the important changes in the cellular organization due to moisture sorption, an affine-based image registration technique is not exhaustive to describe the overall hygro-mechanical behaviour of wood at micrometre scales. Additionally, free tools currently available for non-rigid image registration are not suitable for quantifying the structural deformations of complex hierarchical materials such as wood, leading to errors due to misalignment. In this paper, we adapt an existing non-rigid registration model based on B-spline functions to our case study. The so-modified algorithm combines the concept of feature recognition within specific regions locally distributed in the material with an optimization problem. Results show that the method is able to quantify local deformations induced by moisture changes in tomographic images of wood cell wall with high accuracy. The local deformations provide new important insights in characterizing the swelling behaviour of wood at the cell wall level.

Multitemporal Volume Registration for the Analysis of Rheumatoid Arthritis Evolution in the Wrist

This paper describes a method based on an automatic segmentation process to coregister carpal bones of the same patient imaged at different time points. A rigid registration was chosen to avoid artificial bone deformations and to allow finding eventual differences in the bone shape due to erosion, disease regression, or other eventual pathological signs. The actual registration step is performed on the basis of principal inertial axes of each carpal bone volume, as estimated from the inertia matrix. In contrast to already published approaches, the proposed method suggests splitting the 3D rotation into successive rotations about one axis at a time (the so-called basic or elemental rotations). In such a way, singularity and ambiguity drawbacks affecting other classical methods, for instance, the Euler angles method, are addressed. The proposed method was quantitatively evaluated using a set of real magnetic resonance imaging (MRI) sequences acquired at two different times from healthy wrists and by choosing a direct volumetric comparison as a cost function. Both the segmentation and registration steps are not based on a priori models, and they are therefore able to obtain good results even in pathological cases, as proven by the visual evaluation of actual pathological cases.

Left-Ventricle Segmentation of SPECT Images of Rats

Single-photon emission computed tomography (SPECT) imaging of the heart is helpful to quantify the left-ventricular ejection fraction and study myocardial perfusion scans. However, these evaluations require a 3-D segmentation of the left-ventricular wall on each phase of the cardiac cycle. This paper presents a fast and interactive graph cut method for 3-D segmentation of the left ventricle (LV) of rats in SPECT images. The method is carried out in three steps. First, 3-D sampling of the LV cavity is made in a spherical-cylindrical coordinate system. Then, a graph-cut-based energy minimization procedure provides delineation of the myocardium centerline surface. From there, it is possible to outline the epicardial and endocardial boundaries by considering the second statistical moment of the SPECT images. An important aspect of our method is to always produce anatomically coherent U-shape results. It also relies on only two intuitive parameters regulating the smoothness and the thickness of the segmentation result. Results show not only that our method is statistically as accurate as human experts, but it is one order of magnitude faster than a state-of-the-art method with a processing time of at most 2 s on a 4-D cardiac image after having determined the LV orientation.

Deformable part models for object detection in medical images

BACKGROUND

Object detection in 3-D medical images is often necessary for constraining a segmentation or registration task. It may be a task in its own right as well, when instances of a structure, e.g. the lymph nodes, are searched. Problems from occlusion, illumination and projection do not arise, making the problem simpler than object detection in photographies. However, objects of interest are often not well contrasted against the background. Influence from noise and other artifacts is much stronger and shape and appearance may vary substantially within a class.

METHODS

Deformable models capture the characteristic shape of an anatomic object and use constrained deformation for hypothesing object boundaries in image regions of low or non-existing contrast. Learning these constraints requires a large sample data base. We show that training may be replaced by readily available user knowledge defining a prototypical deformable part model. If structures have a strong part-relationship, or if they may be found based on spatially related guiding structures, or if the deformation is rather restricted, the supporting data information suffices for solving the detection task. We use a finite element model to represent anatomic variation by elastic deformation. Complex shape variation may be represented by a hierarchical model with simpler part variation. The hierarchy may be represented explicitly as a hierarchy of sub-shapes, or implicitly by a single integrated model. Data support and model deformation of the complete model can be represented by an energy term, serving as quality-of-fit function for object detection.

RESULTS

The model was applied to detection and segmentation tasks in various medical applications in 2- and 3-D scenes. It has been shown that model fitting and object detection can be carried out efficiently by a combination of a local and global search strategy using models that are parameterized for the different tasks.

CONCLUSIONS

A part-based elastic model represents complex within-class object variation without training. The hierarchy of parts may specify relationship to neighboring anatomical objects in object detection or a part-decomposition of a complex anatomic structure. The intuitive way to incorporate domain knowledge has a high potential to serve as easily adaptable method to a wide range of different detection tasks in medical image analysis.

Automatic left ventricle segmentation in volumetric SPECT data set by variational level set

INTRODUCTION

Left ventricle (LV) quantification in nuclear medicine images is a challenging task for myocardial perfusion scintigraphy. A hybrid method for left ventricle myocardial border extraction in SPECT datasets was developed and tested to automate LV ventriculography.

METHODS

Automatic segmentation of the LV in volumetric SPECT data was implemented using a variational level set algorithm. The method consists of two steps: (1) initialization and (2) segmentation. Initially, we estimate the initial closed curves in SPECT images using adaptive thresholding and morphological operations. Next, we employ the initial closed curves to estimate the final contour by variational level set. The performance of the proposed approach was evaluated by comparing manually obtained boundaries with automated segmentation contours in 10 SPECT data sets obtained from adult patients. Segmented images by proposed methods were visually compared with manually outlined contours and the performance was evaluated using ROC analysis.

RESULTS

The proposed method and a traditional level set method were compared by computing the sensitivity and specificity of ventricular outlines as well as ROC analysis. The results show that the proposed method can effectively segment LV regions with a sensitivity and specificity of 88.9 and 96.8%, respectively. Experimental results demonstrate the effectiveness and reasonable robustness of the automatic method.

CONCLUSION

A new variational level set technique was able to automatically trace the LV contour in cardiac SPECT data sets, based on the characteristics of the overall region of LV images. Smooth and accurate LV contours were extracted using this new method, reducing the influence of nearby interfering structures including a hypertrophied right ventricle, hepatic or intestinal activity, and pulmonary or intramammary activity.