The potential of a spheroid tumor model composed of cells in different proliferative and metabolic states for the development of new anticancer strategies has been amply demonstrated. However, there is little or no information in the literature on the problems of reproducibility of data originating from experiments using 3D models. Our analyses, carried out using a novel open source software capable of performing an automatic image analysis of 3D tumor colonies, showed that a number of morphology parameters affect the response of large spheroids to treatment. In particular, we found that both spheroid volume and shape may be a source of variability. We also compared some commercially available viability assays specifically designed for 3D models. In conclusion, our data indicate the need for a pre-selection of tumor spheroids of homogeneous volume and shape to reduce data variability to a minimum before use in a cytotoxicity test. In addition, we identified and validated a cytotoxicity test capable of providing meaningful data on the damage induced in large tumor spheroids of up to diameter in 650 μm by different kinds of treatments.

Whatever the target of an experiment in cell biology, cell counting and viability assessment are always computed. The Trypan Blue (TB) assay was proposed about a century ago and is still the most widely used method to perform cell viability analysis. Furthermore, the combined use of TB with a haemocytometer is also considered the standard approach to estimate cell population density. There are numerous research articles reporting the use of TB assays to compute cell number and viability of 2D and 3D cultures. However, the literature still lacks studies regarding the reliability of the TB assay in terms of assessment of its repeatability and reproducibility. We compared the TB assay's measurements obtained by two biologists who analysed 105 different samples in double-blind for a total of 210 counts performed. We measured: (a) the repeatability of the count performed by the same operator; (b) the reproducibility of counts performed by the two operators. There were no significant differences in the results obtained with 2D and 3D cell cultures: we estimated an approximate variability of 5% when the TB assay was used to assess the viability of the culture, and a variability of around 20% when it was used to determine the cell population density. The main aim of this study was to make researchers aware of potential measurement errors when TB is used with a haemocytometer for counting and viability measurements in 2D and 3D cultures. We believe that these results can help researchers to determine whether the expected reliability of the TB assay is compliant with their applications.

In this paper we investigate the feasibility of using an SVM (support vector machine) classifier in our automatic system for the detection of clustered microcalcifications in digital mammograms. SVM is a technique for pattern recognition which relies on the statistical learning theory. It minimizes a function of two terms: the number of misclassified vectors of the training set and a term regarding the generalization classifier capability. We compare the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of our detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features. The SVM classifier gets slightly better results than the MLP one (Az value of 0.963 against 0.958) in the presence of a high number of training data; the improvement becomes much more evident (Az value of 0.952 against 0.918) in training sets of reduced size. Finally, the setting of the SVM classifier is much easier than the MLP one.

Visually track several moving persons engaged in close interactions is known to be a very hard problem, though 3-D approaches based on stereo vision and plan-view maps offer much promise for dealing effectively with major issues such as occlusions and quick changes in body pose and appearance. However, in case of untextured scenes due to homogeneous objects or poor illumination, stereo-based tracking systems rapidly drop their performance. In this work, we present a real time people tracking system able to work even under severe low-lighting conditions. The system relies on a novel active sensor that provides brightness and depth images based on a Time of Flight (TOF) technology. The tracking algorithm is simple yet efficient, being based on geometrical constraints and invariants. Experiments accomplished under changing lighting conditions and involving multiple people closely interacting with each other have proved the reliability of the system.