Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm

Lorenzo-Valdés, Maria; Sanchez-Ortiz, Gerardo I.; Elkington, Andrew G.; Mohiaddin, Raad H.; Rueckert, Daniel

doi:10.1016/j.media.2004.06.005

Cited by 244 publications

(86 citation statements)

References 22 publications

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“…This latter family of methods have been greatly used thanks to their flexibility, especially for this application [9][10][11][12], either on the form of 2D active contours or 3D deformable surfaces, which are more computationally expensive [13,14]. Shape prior information can also be used to guide the segmentation process, under the form of a statistical model, in a variational framework [15], by using active shape and appearance [16][17][18][19][20] or via an atlas, using registration-based segmentation [21,22]. Note that the temporal dimension of cardiac data can be taken into account to improve the segmentation process [13,23].…”

Section: Introductionmentioning

confidence: 99%

Automatic cardiac ventricle segmentation in MR images: a validation study

et al. 2010

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Section: Introductionmentioning

confidence: 99%

Automatic cardiac ventricle segmentation in MR images: a validation study

et al. 2010

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“…[20][21][22][23][24][25] Atlases represent an aggregation of heavily annotated images from multiple subjects. New unsegmented images can be aligned with the deeply annotated atlas in order to map the segmented structures onto the new unseen images.…”

Section: Previous Workmentioning

confidence: 99%

Prostatome: A combined anatomical and disease based MRI atlas of the prostate

Rusu¹,

Bloch²,

Jaffe³

et al. 2014

Med. Phys.

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Purpose:In this work, the authors introduce a novel framework, the anatomically constrained registration (AnCoR) scheme and apply it to create a fused anatomic-disease atlas of the prostate which the authors refer to as the prostatome. The prostatome combines a MRI based anatomic and a histology based disease atlas. Statistical imaging atlases allow for the integration of information across multiple scales and imaging modalities into a single canonical representation, in turn enabling a fused anatomical-disease representation which may facilitate the characterization of disease appearance relative to anatomic structures. While statistical atlases have been extensively developed and studied for the brain, approaches that have attempted to combine pathology and imaging data for study of prostate pathology are not extant. This works seeks to address this gap. Methods: The AnCoR framework optimizes a scoring function composed of two surface (prostate and central gland) misalignment measures and one intensity-based similarity term. This ensures the correct mapping of anatomic regions into the atlas, even when regional MRI intensities are inconsistent or highly variable between subjects. The framework allows for creation of an anatomic imaging and a disease atlas, while enabling their fusion into the anatomic imaging-disease atlas. The atlas presented here was constructed using 83 subjects with biopsy confirmed cancer who had pre-operative MRI (collected at two institutions) followed by radical prostatectomy. The imaging atlas results from mapping the in vivo MRI into the canonical space, while the anatomic regions serve as domain constraints. Elastic co-registration MRI and corresponding ex vivo histology provides "ground truth" mapping of cancer extent on in vivo imaging for 23 subjects. Results: AnCoR was evaluated relative to alternative construction strategies that use either MRI intensities or the prostate surface alone for registration. The AnCoR framework yielded a central gland Dice similarity coefficient (DSC) of 90%, and prostate DSC of 88%, while the misalignment of the urethra and verumontanum was found to be 3.45 mm, and 4.73 mm, respectively, which were measured to be significantly smaller compared to the alternative strategies. As might have been anticipated from our limited cohort of biopsy confirmed cancers, the disease atlas showed that most of the tumor extent was limited to the peripheral zone. Moreover, central gland tumors were typically larger in size, possibly because they are only discernible at a much later stage. Conclusions:The authors presented the AnCoR framework to explicitly model anatomic constraints for the construction of a fused anatomic imaging-disease atlas. The framework was applied to constructing a preliminary version of an anatomic-disease atlas of the prostate, the prostatome. The prostatome could facilitate the quantitative characterization of gland morphology and imaging features of prostate cancer. These techniques, may be applied on a large sample size data set to create...

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“…Tracing not only increases processing time, but also is susceptible to subjectivity of operator-determined border delineation. Algorithms for automated segmentation unconstrained by definition of end diastole or systole could prove to be more accurate, expedient, and reproducible especially in patients with advanced systolic dysfunction [86][87][88][89][90]. As automated contour detection for LV volume analysis software will continue to improve, the speed and accuracy of CMR could ultimately offset the cost differential with echocardiography.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Novel techniques for assessment of left ventricular systolic function

2010

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The evaluation of left ventricular systolic function is one of the most common reasons for referral for a non-invasive cardiac imaging study. In addition to its diagnostic and prognostic value, an assessment of ejection fraction can also be used to guide medical and device therapy. Thus, obtaining an accurate and reproducible assessment of LVEF is essential for patient management. This review will focus on novel multi-modality techniques used for the quantification of left ventricular systolic function. Emerging echocardiography techniques such as three-dimensional echocardiography and strain imaging and their incremental role over traditional 2D imaging will be discussed. In addition, new developments expanding nuclear imaging techniques' evaluation of left ventricular systolic function will be reviewed. Finally, an overview of advances in imaging techniques such as cardiac magnetic resonance and cardiac computed tomography, which now allow for an accurate and highly reproducible assessment of LVEF, will be presented.

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Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm

Cited by 244 publications

References 22 publications

Automatic cardiac ventricle segmentation in MR images: a validation study

Automatic cardiac ventricle segmentation in MR images: a validation study

Prostatome: A combined anatomical and disease based MRI atlas of the prostate

Novel techniques for assessment of left ventricular systolic function

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