Survey PaperA review of segmentation methods in short axis cardiac MR images
Graphical abstract
This paper is a review of fully and semi-automated methods performing cardiac ventricle segmentation in short axis MR images. For this particularly complex segmentation task, prior knowledge is required. An original categorization for cardiac segmentation methods, with a special emphasis on what level of external information is required to constrain segmentation, is presented. Segmentation results are analyzed and methodological as well as medical issues are discussed.
Research highlights
► Comprehensive review on segmentation methods of cardiac short-axis MR images. ► 70 papers between 1993 and 2010 have been reviewed. ► Original categorization based on the level of prior required for segmentation. ► Critical analysis of segmentation results obtained on both cardiac ventricles. ► Highlighting the role of external knowledge and motion information for segmentation.
Introduction
Cardiovascular diseases are the leading cause of death in Western countries (Allender et al., 2008, Lloyd-Jones, 2010). Diagnosis and treatment follow-up of these pathologies can rely on numerous cardiac imaging modalities, which include echography, CT (computerized tomography), coronary angiography and cardiac MRI. Today recognized as a reference modality for the non-invasive assessment of left ventricular function, MRI also supplies accurate information on morphology, muscle perfusion, tissue viability or blood flow, using adequate protocols. The cardiac contractile function can be quantified through ventricle volumes, masses and ejection fraction, by segmenting the left (LV) and right (RV) ventricles from cine MR images. Manual segmentation is a long and tedious task, which requires about 20 min per ventricle by a clinician. Because this task is also prone to intra- and inter-observer variability, there has been a lot of research work about automated segmentation methods. In particular, commercial software packages such as MASS (Medis, Leiden, The Netherlands) (van der Geest et al., 1994) and Argus (Siemens Medical Systems, Germany) (O’Donnell et al., 2006) are today available for automatic ventricle delineation. Even though processing time has been greatly reduced, the provided contour detection still needs to be improved to equal manual contour tracing (François et al., 2004, Mahnken et al., 2006). While reviews on cardiac image analysis (Suri, 2000, Frangi et al., 2001) and medical image segmentation (Suri et al., 2001, Pham et al., 2000) exist, we have focused on methods dedicated to cardiac MR segmentation: the particular shape of both ventricles, as well as MR characteristics, have required specific developments. Despite more than 15 years of research, the problem is still open, as shown by the holding of a segmentation contest on the LV in 2009 during the MICCAI conference1, and remains completely unsolved for the RV.
In the present paper, we will review automatic and semi-automatic segmentation methods of cine MR images of the cardiac ventricles, using the short-axis view, the most common imaging plane to assess the cardiac function. We wish to provide the reader with (i) major challenges linked to this segmentation task, (ii) a state-of-the art of cardiac segmentation methods, including debut methods and current ones, and (iii) future trends in this field. This paper is intended for researchers in the field of cardiac segmentation, but also to image processing and pattern recognition researchers interested to see how different segmentation techniques apply for a given application. The remaining of the paper is as follows. Short-axis MR images and the challenge of their segmentation are presented in Section 2. In Section 3, a categorization for segmentation methods is proposed and justified. Segmentation methods are presented in Section 4 and their results are discussed in Section 5. At last conclusion and perspectives are drawn in Section 6.
Section snippets
Description of short-axis MR images
The complexity of segmenting heart chambers and myocardium mainly relies on heart anatomy and MRI acquisition specificity. The LV function consists in pumping the oxygenated blood to the aorta and consequently to the systemic circuit. The LV cavity has a well-known shape of ellipsoid (Fig. 1) and is surrounded by the myocardium, whose normal values for thickness range from 6 to 16 mm. On the contrary, the RV has a complex crescent shape. It also faces lower pressure to eject blood to the lungs
Overview of segmentation methods
For this review, we have been considering the papers among peer-reviewed publications, that included (i) a segmentation method for delineating the LV, the RV, or both, (ii) qualitative or quantitive assessment of the method and (iii) illustrations on cardiac MR data. All 70 reviewed papers have been listed in Table 2. Classifying these methods is not a trivial task. A common categorization for medical image segmentation includes thresholding, edge-based and region-based approaches, pixel-based
Automatic localization of the heart
As mentioned above, a ROI centered on the heart is generally extracted from the original MR image, in order not to process the whole image, thus decreasing computational load. Automatic approaches are twofold: time-based approaches, that take advantage of the fact that the heart is the only moving organ in the images, or object detection techniques. They both have in common the use of the Hough transform, that allows to detect the position of the LV thanks to its circular aspect. Note that
Methodological issues
Now let us examine how issues presented in Section 3.2 have been dealt with, according to the type of methods, by commenting Table 2. Among the 70 papers that have been reviewed in this paper, only three studies are entirely devoted to the RV, while a quarter of the rest of the papers show segmentation results on both ventricles. As mentioned above, physical characteristics of the RV as well as its lesser vital function has restrained research efforts on its segmentation. Nonetheless there is a
Conclusion and perspectives
This paper has been presenting segmentation methods in cardiac MRI. It seemed important to us to integrate the recent developments of the last decade about prior knowledge based segmentation, almost 10 years after Frangi et al.’s comprehensive review (Frangi et al., 2001). We have proposed a categorization for these methods, highlighting the key role of the type of prior information used during segmentation, and have distinguished three levels of information: (i) no information is used, but our
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