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Biomedical subjects

Dónal B Downey

Publications and source records attributed to Dónal B Downey.

8 recordsLinked to original sources
PubMed2006

Prostate boundary segmentation from ultrasound images using 2D active shape models: optimisation and extension to 3D.

Boundary outlining, or segmentation, of the prostate is an important task in diagnosis and treatment planning for prostate cancer. This paper describes an algorithm based on two-dimensional (2D) active shape models (ASM) for semi-automatic segmentation of the prostate boundary from ultrasound images. Optimisation of the 2D ASM for prostatic ultrasound was done first by examining ASM construction and image search parameters. Extension of the algorithm to three-dimensional (3D) segmentation was then done using rotational-based slicing. Evaluation of the 3D segmentation algorithm used distance- and volume-based error metrics to compare algorithm generated boundary outlines to gold standard (manually generated) boundary outlines. Minimum description length landmark placement for ASM construction, and specific values for constraints and image search were found to be optimal. Evaluation of the algorithm versus gold standard boundaries found an average mean absolute distance of 1.09+/-0.49 mm, an average percent absolute volume difference of 3.28+/-3.16%, and a 5x speed increase versus manual segmentation.

Algorithms↗
PubMed2005

A new three-dimensional ultrasound microimaging technology for preclinical studies using a transgenic prostate cancer mouse model.

Prostate cancer is the most common cancer in adult men in North America. Preclinical studies of prostate cancer employ genetically engineered mouse models, because prostate cancer does not occur naturally in rodents. Widespread application of these models has been limited because autopsy was the only reliable method to evaluate treatment efficacy in longitudinal studies. This article reports the first use of three-dimensional ultrasound microimaging for measuring tumor progression in a genetically engineered mouse model, the 94-amino acid prostate secretory protein gene-directed transgenic prostate cancer model. Qualitative comparisons of three-dimensional ultrasound images with serial histology sections of prostate tumors show the ability of ultrasound to accurately depict the size and shape of malignant masses in live mice. Ultrasound imaging identified tumors ranging from 2.4 to 14 mm maximum diameter. The correlation coefficient of tumor diameter measurements done in vivo with three-dimensional ultrasound and at autopsy was 0.998. Prospective tumor detection sensitivity and specificity were both >90% when diagnoses were based on repeated ultrasound examinations done on separate days. Representative exponential growth curves constructed via longitudinal ultrasound imaging indicated volume doubling times of 5 and 13 days for two prostate tumors. Compared with other microimaging and molecular imaging modalities, the application of three-dimensional ultrasound imaging to prostate cancer in mice showed advantages, such as high spatial resolution and contrast in soft tissue, fast and uncomplicated protocols, and portable and economical equipment that will likely enable ultrasound to become a new microimaging modality for mouse preclinical trial studies.

Adenocarcinoma↗
PubMed2004

3D ultrasound imaging of the carotid arteries.

Although ultrasonography is an important cost-effective imaging modality, technical improvements are needed before its full potential is realized for accurate and reproducible monitoring of carotid disease and plaque burden. 2D viewing of 3D anatomy, using conventional ultrasonography limits our ability to quantify and visualize carotid disease and is partly responsible for the reported variability in diagnosis and monitoring of disease progression. Efforts of investigators have focused on overcoming these deficiencies by developing 3D ultrasound imaging techniques that are capable of acquiring B-mode, color Doppler and power Doppler images of the carotid arteries using existing conventional ultrasound systems, reconstructing the information into 3D images, and then allowing interactive viewing of the 3D images on inexpensive desktop computers. In addition, the availability of 3D ultrasound images of the carotid arteries has allowed the development of techniques to quantify plaque volume and surface morphology as well as allowing registration with other 3D imaging modalities. This paper describes 3D ultrasound imaging techniques used to image the carotid arteries and summarizes some of the developments aimed at quantifying plaque volume and morphology.

Carotid Arteries↗
PubMed2003

Three-dimensional ultrasound imaging and its use in quantifying organ and pathology volumes.

Although ultrasonography is an important cost-effective imaging modality, technical improvements are needed before its full potential is realized for accurate and reproducible monitoring of disease progression or regression. Two-dimensional viewing of three-dimensional anatomy, using conventional ultrasonography, limits our ability to quantify and visualize pathology and is partly responsible for the reported variability in diagnosis and monitoring of disease progression. Efforts of investigators have focused on overcoming these deficiencies by developing 3D ultrasound imaging techniques that are capable of acquiring B-mode images using existing conventional ultrasound systems, reconstructing the information into 3D images, and then allowing interactive viewing of the 3D images on inexpensive desktop computers. In addition, the availability of 3D ultrasound images has allowed the development of manual and semi-automated techniques to quantify normal and abnormal anatomical volumes. In this paper we review our semi-automated 3D segmentation approaches for segmenting the surface of the carotid arteries and plaques, and segmenting the prostate. These techniques demonstrate that efficient segmentation techniques can be used with 3D ultrasound images to quantify anatomical organ volumes and morphology.

Carotid Arteries↗
PubMed2003

Testing and optimization of a semiautomatic prostate boundary segmentation algorithm using virtual operators.

Image analysis tasks such as size measurement and landmark-based registration require the user to select control points in an image. The output of such algorithms depends on the choice of control points. Since the choice of points varies from one user to the next, the requirement for user input introduces variability into the output of the algorithm. In order to test and/or optimize such algorithms, it is necessary to assess the multiplicity of outputs generated by the algorithm in response to a large set of inputs; however, the input of data requires substantial time and effort from multiple users. In this paper we describe a method to automate the testing and optimization of algorithms using "virtual operators," which consist of a set of spatial distributions describing how actual users select control points in an image. In order to construct the virtual operator, multiple users must repeatedly select control points in the image on which testing is to be performed. Once virtual operators are generated, control points for initializing the algorithm can be generated from them using a random number generator. Although an initial investment of time is required from the users in order to construct the virtual operator, testing and optimization of the algorithm can be done without further user interaction. We illustrate the construction and use of virtual operators by testing and optimizing our prostate boundary segmentation algorithm. The algorithm requires the user to select four control points on the prostate as input.

Algorithms↗
PubMed2003

Prostate boundary segmentation from 3D ultrasound images.

Segmenting, or outlining the prostate boundary is an important task in the management of patients with prostate cancer. In this paper, an algorithm is described for semiautomatic segmentation of the prostate from 3D ultrasound images. The algorithm uses model-based initialization and mesh refinement using an efficient deformable model. Initialization requires the user to select only six points from which the outline of the prostate is estimated using shape information. The estimated outline is then automatically deformed to better fit the prostate boundary. An editing tool allows the user to edit the boundary in problematic regions and then deform the model again to improve the final results. The algorithm requires less than 1 min on a Pentium III 400 MHz PC. The accuracy of the algorithm was assessed by comparing the algorithm results, obtained from both local and global analysis, to the manual segmentations on six prostates. The local difference was mapped on the surface of the algorithm boundary to produce a visual representation. Global error analysis showed that the average difference between manual and algorithm boundaries was -0.20 +/- 0.28 mm, the average absolute difference was 1.19 +/- 0.14 mm, the average maximum difference was 7.01 +/- 1.04 mm, and the average volume difference was 7.16% +/- 3.45%. Variability in manual and algorithm segmentation was also assessed: Visual representations of local variability were generated by mapping variability on the segmentation mesh. The mean variability in manual segmentation was 0.98 mm and in algorithm segmentation was 0.63 mm and the differences of about 51.5% of the points comprising the average algorithm boundary are insignificant (P < or = 0.01) to the manual average boundary.

Algorithms↗
PubMed2002

Sonography of peripheral nerves: technique, anatomy, and pathology.

Sonography provides a unique method for evaluating peripheral nerves because of its high spatial resolution, its ability to follow structures over long distances in a limb, and its dynamic nature that allows movement of patient and transducer. When combined with the wide availability and relatively low cost, sonography is an excellent modality for peripheral nerve assessment. The authors review the techniques used in assessment of peripheral nerves, the relevant anatomy, and common pathologic processes.

Journal Article↗