Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images.

Subsolid pulmonary nodules occur less often than solid pulmonary nodules, but show a much higher malignancy rate. Therefore, accurate detection of this type of pulmonary nodules is crucial. In this work, a computer-aided detection (CAD) system for subsolid nodules in computed tomography images is presented and evaluated on a large data set from a multi-center lung cancer screening trial.

Evaluation of the effect of computer-aided classification of benign and malignant lesions on reader performance in automated three-dimensional breast ultrasound.

Rationale And Objectives: To investigate the effect of a newly developed computer-aided diagnosis (CAD) system on reader interpretation of breast lesions in automated three-dimensional (3D) breast ultrasound.

Materials And Methods: A CAD system was developed to differentiate malignant lesions from benign lesions including automated lesion segmentation in three dimensions; extraction of lesion features such as spiculation, margin contrast, and posterior acoustic behavior; and a classification stage. Eighty-eight patients with breast lesions were included for an observer study: 47 lesions were malignant and 41 were benign.

Computer-aided detection of ground glass nodules in thoracic CT images using shape, intensity and context features.

Ground glass nodules (GGNs) occur less frequent in computed tomography (CT) scans than solid nodules but have a much higher chance of being malignant. Accurate detection of these nodules is therefore highly important. A complete system for computer-aided detection of GGNs is presented consisting of initial segmentation steps, candidate detection, feature extraction and a two-stage classification process.

Model-Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition techniques for computing enhanced visualizations of suspicious lesions in breast MRI data. These techniques represent an important component of future sophisticated computer-aided diagnosis (CAD) systems and support the visual exploration of spatial and temporal features of DCE-MRI data stemming from patients with confirmed lesion diagnosis.

Reagent-free automatic cell viability determination using neural networks based machine vision and dark-field microscopy in Saccharomyces cerevisiae.

Fermentation industries require in-situ real-time monitoring of cell viability during fermentation processes. For this purpose, reagent-free approaches are desired because they can be used for in situ analysis and reduce the system's complexity. We have developed an automatic way of determining cell viability via analysis of time-lapse image sequences taken by dark field microscopy without the aid of any additional reagents.

An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data.

Libraries of synthetic stationary-phase and stress promoters as a tool for fine-tuning of expression of recombinant proteins in Escherichia coli.

Due to their induction characteristics stationary-phase promoters have a great potential in biotechnological processes for the production of heterologous proteins on a large-scale. In order to broaden the utility of stationary-phase promoters in bacterial expression systems and to create novel promoters induced by metabolic conditions, a library of synthetic stationary-phase/stress promoters for Escherichia coli was constructed. For designing the promoters the known -10 consensus sequence as well as the extended -10 region and an A/T-rich region downstream of the -10 region were kept constant, while sequences from -37 to -14 were partially or completely randomized.

Image fusion for dynamic contrast enhanced magnetic resonance imaging.

Background: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of information is a challenging task for the human observer. This task may be assisted with the use of image fusion algorithms.

Human vs machine: evaluation of fluorescence micrographs.

To enable high-throughput screening of molecular phenotypes, multi-parameter fluorescence microscopy is applied. Object of our study is lymphocytes which invade human tissue. One important basis for our collaborative project is the development of methods for automatic and accurate evaluation of fluorescence micrographs.