Computer-aided segmentation of anatomical structures in medical images is a valuable tool for efficient radiation therapy planning (RTP). As delineation errors highly affect the radiation oncology treatment, it is crucial to delineate geometric structures accurately. In this paper, a semi-automatic segmentation approach for computed tomography (CT) images, based on watershed and graph-cuts methods, is presented. The watershed pre-segmentation groups small areas of similar intensities in homogeneous labels, which are subsequently used as input for the graph-cuts algorithm. This methodology does not require of prior knowledge of the structure to be segmented; even so, it performs well with complex shapes and low intensity. The presented method also allows the user to add foreground and background strokes in any of the three standard orthogonal views – axial, sagittal or coronal - making the interaction with the algorithm easy and fast. Hence, the segmentation information is propagated within the whole volume, providing a spatially coherent result. The proposed algorithm has been evaluated using 9 CT volumes, by comparing its segmentation performance over several organs - lungs, liver, spleen, heart and aorta - to those of manual delineation from experts. A Dice´s coefficient higher than 0.89 was achieved in every case. That demonstrates that the proposed approach works well for all the anatomical structures analyzed. Due to the quality of the results, the introduction of the proposed approach in the RTP process will be a helpful tool for organs at risk (OARs) segmentation.
Accurate delineation of organs at risk (OAR) is required for radiation treatment planning (RTP). However, it is a very time consuming and tedious task. The use in clinic of image guided radiation therapy (IGRT) becomes more and more popular, thus increasing the need of (semi-)automatic methods for delineation of the OAR. In this work, an interactive segmentation approach to delineate OAR is proposed and validated. The method is based on the combination of watershed transformation, which groups small areas of similar intensities in homogeneous labels, and graph cuts approach, which uses these labels to create the graph. Segmentation information can be added in any view – axial, sagittal or coronal -, making the interaction with the algorithm easy and fast. Subsequently, this information is propagated within the whole volume, providing a spatially coherent result. Manual delineations made by experts of 6 OAR - lungs, kidneys, liver, spleen, heart and aorta – over a set of 9 computed tomography (CT) scans were used as reference standard to validate the proposed approach. With a maximum of 4 interactions, a Dice similarity coefficient (DSC) higher than 0.87 was obtained, which demonstrates that, with the proposed segmentation approach, only few interactions are required to achieve similar results as the ones obtained manually. The integration of this method in the RTP process may save a considerable amount of time, and reduce the annotation complexity.
Cardiac magnetic resonance perfusion imaging (CMR) and computed tomography angiography (CTA) are widely used to
assess heart disease. CMR is used to measure the global and regional myocardial function and to evaluate the presence of
ischemia; CTA is used for diagnosing coronary artery disease, such as coronary stenoses. Nowadays, the hemodynamic
significance of coronary artery stenoses is determined subjectively by combining information on myocardial function with
assumptions on coronary artery territories. As the anatomy of coronary arteries varies greatly between individuals, we
developed a patient-specific tool for relating CTA and perfusion CMR data. The anatomical and functional information
extracted from CTA and CMR data are combined into a single frame of reference. Our graphical user interface provides
various options for visualization. In addition to the standard perfusion Bull's Eye Plot (BEP), it is possible to overlay a 2D
projection of the coronary tree on the BEP, to add a 3D coronary tree model and to add a 3D heart model. The perfusion
BEP, the 3D-models and the CTA data are also interactively linked. Using the CMR and CTA data of 14 patients, our
tool directly established a spatial correspondence between diseased coronary artery segments and myocardial regions with
abnormal perfusion. The location of coronary stenoses and perfusion abnormalities were visualized jointly in 3D, thereby
facilitating the study of the relationship between the anatomic causes of a blocked artery and the physiological effects on
the myocardial perfusion. This tool is expected to improve diagnosis and therapy planning of early-stage coronary artery
disease.
Computed tomography angiography (CTA), a non-invasive imaging technique, is becoming increasingly popular for cardiac
examination, mainly due to its superior spatial resolution compared to MRI. This imaging modality is currently widely
used for the diagnosis of coronary artery disease (CAD) but it is not commonly used for the diagnosis of ventricular and
atrial function. In this paper, we present a fully automatic method for segmenting the whole heart (i.e. the outer surface of
the myocardium) and cardiac chambers from CTA datasets. Cardiac chamber segmentation is particularly valuable for the
extraction of ventricular and atrial functional information, such as stroke volume and ejection fraction. With our approach,
we aim to improve the diagnosis of CAD by providing functional information extracted from the same CTA data, thus not
requiring additional scanning. In addition, the whole heart segmentation method we propose can be used for visualization
of the coronary arteries and for obtaining a region of interest for subsequent segmentation of the coronaries, ventricles and
atria. Our approach is based on multi-atlas segmentation, and performed within a non-rigid registration framework. A
leave-one-out quantitative validation was carried out on 8 images. The method showed a high accuracy, which is reflected
in both a mean segmentation error of 1.05±1.30 mm and an average Dice coefficient of 0.93. The robustness of the method
is demonstrated by successfully applying the method to 243 additional datasets, without any significant failure.
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