Ground Truth Manual Segmentation Research Articles

Intraoperative segmentation of iodine and palladium radioactive sources in C-arm images

Dynamic dosimetry is becoming the standard to evaluate the quality of radioactive implants during brachytherapy. For this, it is essential to obtain a 3D visualization of the implanted seeds and their relative position to the prostate. A method was developed to obtain a robust and precise segmentation of seeds in C-arm images, and this approach was tested using clinical datasets. A region-based implicit active contour approach was used to delineate implanted seeds. Then, a template-based matching was employed to segment iodine implants whereas a K-means algorithm is implemented to resolve palladium seed clusters. To validate the method, 55 C-arm images from 10 patients were used for the segmentation of iodine sources, whereas 225 C-arm images from 16 patients were used for the palladium case. Compared to manual ground truth segmentation of 6,002 iodine seeds and 15,354 palladium seeds, 98.7 % of iodine sources were automatically detected and declustered showing a false-positive rate of only 1.7 %. A total of 98.7 % of palladium sources were automatically detected and declustered with a false-positive rate of only 2.0 %. An automated segmentation method was developed that is able to perform the identification and annotation processes of seeds on par with a human expert. This method was shown to be robust and suitable for integration in the dynamic dosimetry workflow of prostate brachytherapy interventions.

Automated segmentation of CBCT image using spiral CT atlases and convex optimization.

Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.

Three‐dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

Drosophila embryogenesis is an established model to investigate mechanisms and genes related to cell divisions in an intact multicellular organism. Progression through the cell cycle phases can be monitored in vivo using fluorescently labeled fusion proteins and time-lapse microscopy. To measure cellular properties in microscopic images, accurate and fast image segmentation methods are a critical prerequisite. To quantify static and dynamic features of interphase nuclei and mitotic chromosomes, we developed a three-dimensional (3D) segmentation method based on multiple level sets. We tested our method on 3D time-series images of live embryos expressing histone-2Av-green fluorescence protein. Our method is robust to low signal-to-noise ratios inherent to high-speed imaging, fluorescent signals in the cytoplasm, and dynamic changes of shape and texture. Comparisons with manual ground-truth segmentations showed that our method achieves more than 90% accuracy on the object as well as voxel levels and performs consistently throughout all cell cycle phases and developmental stages from syncytial blastoderm to postblastoderm mitotic domains.

A comparison of MR based segmentation methods for measuring brain atrophy progression

Automated brain segmentation methods with a good precision and accuracy are required to detect subtle changes in brain volumes over time in clinical applications. However, the ability of established methods such as SIENA, US and kNN to estimate brain volume change have not been compared on the same data, nor been evaluated with ground-truth manual segmentations. We compared measurements of brain volume change between SIENA, US and kNN in terms of precision (repeatability) and accuracy (ground-truth) using one baseline and two repeated follow-up 1.5T MRI scans after 4years of 10 subjects. The coefficient of repeatability (brain volume/volume change) was larger for US (29.6cc/2.84%) than for kNN (4.9cc/0.31%) and SIENA (−/0.92%). In terms of absolute brain volume measurements US and kNN showed good correlation with the manual segmentations and with each other (all Spearman's correlation coefficients ρ≥0.96; all p<0.001). Concerning brain volume changes, SIENA showed a good (ρ=0.82; p=0.004), kNN a moderate (ρ=0.60; p=0.067) and US a weak (ρ=0.50; p=0.138) correlation with the manual segmentations. For measurements of volume change, SIENA–US (mean correlation coefficient and p-value: ρ=0.28; p=0.442) and US–kNN (ρ=0.17; p=0.641) showed a weak correlation, but correlation was fairly good for kNN–SIENA (ρ=0.65; p=0.048). In conclusion, US and kNN showed a good precision, accuracy and comparability for brain volume measurements. For measurements of volume change, SIENA showed the best performance. kNN is a good alternative if volume change measurements of other brain structures are required.

A comparative study of existing metrics for 3D-mesh segmentation evaluation

In this paper, we present an extensive experimental comparison of existing similarity metrics addressing the quality assessment problem of mesh segmentation. We introduce a new metric, named the 3D Normalized Probabilistic Rand Index (3D-NPRI), which outperforms the others in terms of properties and discriminative power. This comparative study includes a subjective experiment with human observers and is based on a corpus of manually segmented models. This corpus is an improved version of our previous one (Benhabiles et al. in IEEE International Conference on Shape Modeling and Application (SMI), 2009). It is composed of a set of 3D-mesh models grouped in different classes associated with several manual ground-truth segmentations. Finally the 3D-NPRI is applied to evaluate six recent segmentation algorithms using our corpus and the Chen et al.’s (ACM Trans. Graph. (SIGGRAPH), 28(3), 2009) corpus.

An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation

Purpose We present a new algorithm for nearly automatic liver segmentation and volume estimation from abdominal Computed Tomography Angiography (CTA) images and its validation. Materials and methods Our hybrid algorithm uses a multiresolution iterative scheme. It starts from a single user-defined pixel seed inside the liver, and repeatedly applies smoothed Bayesian classification to identify the liver and other organs, followed by adaptive morphological operations and active contours refinement. We evaluate the algorithm with two retrospective studies on 56 validated CTA images. The first study compares it to ground-truth manual segmentation and semi-automatic and automatic commercial methods. The second study uses the public data-set SLIVER07 and its comparison methodology. Results We achieved for both studies, correlations of 0.98 and 0.99 for liver volume estimation, with mean volume differences of 5.36 and 2.68% with respect to manual groundtruth estimation, and mean volume variability for different initial seeds of 0.54 and 0.004%, respectively. For the second study, our algorithm scored 71.8 and 67.87 for the training and test datasets, which compares very favorably with other semi-automatic methods. Conclusions Our algorithm requires minimal interaction by a non-expert user, is accurate, efficient, and robust to initial

Multi-classifier framework for atlas-based image segmentation

Three different systematic approaches to generate multiple classifiers in atlas-based biomedical image segmentation are compared. Different atlases, as well as different parametrization of the registration algorithm, lead to different atlas-based classifiers. The classifier outputs are combined and compared to a manual ground truth segmentation. Classifier combination consistently improved classification accuracy with the biggest improvement from multiple atlases. We conclude that multi-classifier techniques have a natural application to atlas-based segmentation and increase classification accuracy in real-world segmentation problems.