Fecal tagging preparations are attracting notable interest as a way to increase patients' compliance to virtual colonoscopy.
Patient-friendly preparations, however, often result in less homogeneous tagging. Electronic cleansing algorithms should
be capable of dealing with such preparations and yield good quality 2D and 3D images; moreover, successful electronic
cleansing lays the basis for the application of Computer Aided Detection schemes. In this work, we present a cleansing
algorithm based on an adaptive remapping procedure, which is based on a model of how partial volume affects both the
air-tissue and the soft-tissue interfaces. Partial volume at the stool-soft tissue interface is characterized in terms of the
local characteristics of tagged regions, in order to account for variations in tagging intensity throughout the colon. The
two models are then combined in order to obtain a remapping equation relating the observed intensity to the that of the
cleansed colon. The electronic cleansed datasets were then processed by a CAD scheme composed of three main steps:
colon surface extraction, polyp candidate segmentation through curvature-based features, and linear classifier-based
discrimination between true polyps and false alarms. Results obtained were compared with a previous version of the
cleansing algorithm, in which a simpler remapping procedure was used. Performances are increased both in terms of the
visual quality of the 2D cleansed images and 3D rendered volumes, and of CAD performances on a sameday FT virtual
colonoscopy dataset.
The successful application of Computer Aided Detection schemes to CT Colonography depends not only on their
performances in terms of sensitivity and specificity, but also on the interaction with the radiologist, and thus ultimately
on factors such as the nature of CAD prompts and the reading paradigm. Fecal tagging is emerging as a widely accepted
technique for patient preparation, and patient-friendlier schemes are being proposed in an effort to increase compliance
to screening programs; the interaction between CAD and FT regimens should likewise be taken into account. In this
scenario, an analysis of the characteristics of CAD prompts is of paramount importance in order to guide further
research, both from clinical and technical viewpoints. The CAD scheme analyzed in this paper is essentially composed
of five steps: electronic cleansing, colon surface extraction, polyp candidate segmentation, pre-filtering of residual
tagged stool and classification of the generated candidates in true polyps vs. false alarms. False positives were divided
into six categories: untagged and tagged solid stool, haustral folds, extra-colonic candidates, ileocecal valve and
cleansing artifacts. A full cathartic preparation was compared with a semi-cathartic regimen with sameday fecal tagging,
which is characterized by higher patient acceptance but also higher inhomogeneity. The distribution of false positives at
segmentation reflects the quality of preparation, as more inhomogeneous tagging results in a higher number of untagged
solid stool and cleansing artifacts.
KEYWORDS: Image segmentation, Computer aided diagnosis and therapy, Breast, Image registration, Mammography, Image processing, Medical imaging, Current controlled current source, Image restoration, Magnetic resonance imaging
Dynamic Contrast Enhanced MRI (DCE-MRI) has today a well-established role, complementary to routine imaging techniques for breast cancer diagnosis such as mammography. Despite its undoubted clinical advantages, DCE-MRI data analysis is time-consuming and Computer Aided Diagnosis (CAD) systems are required to help radiologists. Segmentation is one of the key step of every CAD image processing pipeline, but most techniques available require human interaction.
We here present the preliminary results of a fully automatic lesion detection method, capable of dealing with fat suppression image acquisition sequences, which represents a challenge for image processing algorithms due to the low SNR. The method is based on four fundamental steps: registration to correct for motion artifacts; anatomical segmentation to discard anatomical structures located outside clinically interesting lesions; lesion detection to select enhanced areas and false positive reduction based on morphological and kinetic criteria. The testing set was composed by 13 cases and included 27 lesions (10 benign and 17 malignant) of diameter > 5 mm. The system achieves a per-lesion sensitivity of 93%, while yielding an acceptable number of false positives (26 on average). The results of our segmentation algorithm were verified by visual inspection, and qualitative comparison with a manual segmentation yielded encouraging results.
One of the key factors which may lead to a greater patient compliance in virtual colonoscopy is a well tolerated bowel preparation; there is evidence that this may be obtained by faecal tagging techniques. In so-called "sameday faecal tagging" (SDFT) preparations, iodine contrast material is administered on the day of the exam in a hospital ward. The administration of oral contrast on the day of the procedure, however, often results in a less homogenous marking of faecal residues and computer aided-detection (CAD) systems must be able to treat these kinds of preparations. The aim of this work is to present a CAD scheme capable of achieving good performances on CT datasets obtained using SDFT, both in terms of sensitivity and specificity. The electronically cleansed datasets is processed by a scheme composed of three steps: colon surface extraction, polyp candidate segmentation through curvature-based features and discrimination between true polyps and false alarms. The system was evaluated on a dataset including 102 patients from three different centers. A specificity of 8.2 false positives per scan was obtained at a 100% sensitivity for polyps larger than 10 mm. In conclusion CAD schemes for SDFT may be designed to obtain high performances.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.