Carlos Ortiz-de-Solorzano

Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data

It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected.

3D reconstruction of histological sections: Application to mammary gland tissue

In this article, we present a novel method for the automatic 3D reconstruction of thick tissue blocks from 2D histological sections. The algorithm completes a high‐content (multiscale, multifeature) imaging system for simultaneous morphological and molecular analysis of thick tissue samples. This computer‐based system integrates image acquisition, annotation, registration, and three‐dimensional reconstruction. We present an experimental validation of this tool using both synthetic and real data. In particular, we present the 3D reconstruction of an entire mouse mammary gland and demonstrate the integration of high‐resolution molecular data. Microsc. Res. Tech. 73:1019–1029, 2010.

A Benchmark for Comparison of Cell Tracking Algorithms

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge. Contact: codesolorzano@unav.es Supplementary information: Supplementary data are available at Bioinformatics online.

Sustained release of VEGF through PLGA microparticles improves vasculogenesis and tissue remodeling in an acute myocardial ischemia-reperfusion model.

The use of pro-angiogenic growth factors in ischemia models has been associated with limited success in the clinical setting, in part owing to the short lived effect of the injected cytokine. The use of a microparticle system could allow localized and sustained cytokine release and consequently a prolonged biological effect with induction of tissue revascularization. To assess the potential of VEGF(165) administered as continuous release in ischemic disease, we compared the effect of delivery of poly(lactic-co-glycolic acid) (PLGA) microparticles (MP) loaded with VEGF(165) with free-VEGF or control empty microparticles in a rat model of ischemia-reperfusion. VEGF(165) loaded microparticles could be detected in the myocardium of the infarcted animals for more than a month after transplant and provided sustained delivery of active protein in vitro and in vivo. One month after treatment, an increase in angiogenesis (small caliber caveolin-1 positive vessels) and arteriogenesis (α-SMA-positive vessels) was observed in animals treated with VEGF microparticles (p<0.05), but not in the empty microparticles or free-VEGF groups. Correlating with this data, a positive remodeling of the heart was also detected in the VEGF-microparticle group with a significantly greater LV wall thickness (p<0.01). In conclusion, PLGA microparticle is a feasible and promising cytokine delivery system for treatment of myocardial ischemia. This strategy could be scaled up and explored in pre-clinical and clinical studies.

Consistent and elastic registration of histological sections using vector-spline regularization

Here we present a new image registration algorithm for the alignment of histological sections that combines the ideas of B-spline based elastic registration and consistent image registration, to allow simultaneous registration of images in two directions (direct and inverse). In principle, deformations based on B-splines are not invertible. The consistency term overcomes this limitation and allows registration of two images in a completely symmetric way. This extension of the elastic registration method simplifies the search for the optimum deformation and allows registering with no information about landmarks or deformation regularization. This approach can also be used as the first step to solve the problem of group-wise registration.

Adiposoft: automated software for the analysis of white adipose tissue cellularity in histological sections

The accurate estimation of the number and size of cells provides relevant information on the kinetics of growth and the physiological status of a given tissue or organ. Here, we present Adiposoft, a fully automated open-source software for the analysis of white adipose tissue cellularity in histological sections. First, we describe the sequence of image analysis routines implemented by the program. Then, we evaluate our software by comparing it with other adipose tissue quantification methods, namely, with the manual analysis of cells in histological sections (used as gold standard) and with the automated analysis of cells in suspension, the most commonly used method. Our results show significant concordance between Adiposoft and the other two methods. We also demonstrate the ability of the proposed method to distinguish the cellular composition of three different rat fat depots. Moreover, we found high correlation and low disagreement between Adiposoft and the manual delineation of cells. We conclude that Adiposoft provides accurate results while considerably reducing the amount of time and effort required for the analysis.

Inhibition of telomerase activity preferentially targets aldehyde dehydrogenase-positive cancer stem-like cells in lung cancer

BackgroundMortality rates for advanced lung cancer have not declined for decades, even with the implementation of novel chemotherapeutic regimens or the use of tyrosine kinase inhibitors. Cancer Stem Cells (CSCs) are thought to be responsible for resistance to chemo/radiotherapy. Therefore, targeting CSCs with novel compounds may be an effective approach to reduce lung tumor growth and metastasis. We have isolated and characterized CSCs from non-small cell lung cancer (NSCLC) cell lines and measured their telomerase activity, telomere length, and sensitivity to the novel telomerase inhibitor MST312.ResultsThe aldehyde dehydrogenase (ALDH) positive lung cancer cell fraction is enriched in markers of stemness and endowed with stem cell properties. ALDH+ CSCs display longer telomeres than the non-CSC population. Interestingly, MST312 has a strong antiproliferative effect on lung CSCs and induces p21, p27 and apoptosis in the whole tumor population. MST312 acts through activation of the ATM/pH2AX DNA damage pathway (short-term effect) and through decrease in telomere length (long-term effect). Administration of this telomerase inhibitor (40 mg/kg) in the H460 xenograft model results in significant tumor shrinkage (70% reduction, compared to controls). Combination therapy consisting of irradiation (10Gy) plus administration of MST312 did not improve the therapeutic efficacy of the telomerase inhibitor alone. Treatment with MST312 reduces significantly the number of ALDH+ CSCs and their telomeric length in vivo.ConclusionsWe conclude that antitelomeric therapy using MST312 mainly targets lung CSCs and may represent a novel approach for effective treatment of lung cancer.

Treatment of Pancreatic Cancer With an Oncolytic Adenovirus Expressing Interleukin-12 in Syrian Hamsters

Pancreatic cancer is an aggressive malignancy resistant to most conventional and experimental therapies, including conditionally replicative adenoviruses (CRAds). The incorporation of immunostimulatory genes such as interleukin-12 (IL-12) in these viruses may overcome some of their limitations, but evaluation of such vectors requires suitable preclinical models. We describe a CRAd in which replication is dependent on hypoxia-inducible factor (HIF) activity and alterations of the pRB pathway in cancer cells. Transgenes (luciferase or IL-12) were incorporated into E3 region of the virus using a selective 6.7K/gp19K deletion. A novel permissive model of pancreatic cancer developed in immunocompetent Syrian hamsters was used for in vivo analysis. We show that, in contrast with nonreplicating adenoviruses (NR-Ad), active viral production and enhanced transgene expression took place in vivo. A single intratumor inoculation of the CRAd expressing IL-12 (Ad-DHscIL12) achieved a potent antitumor effect, whereas higher doses of replication-competent adenoviruses carrying luciferase did not. Compared to a standard NR-Ad expressing IL-12, Ad-DHscIL12 was less toxic in hamsters, with more selective tumor expression and shorter systemic exposure to the cytokine. We conclude that the expression of IL-12 in the context of a hypoxia-inducible oncolytic adenovirus is effective against pancreatic cancer in a relevant animal model.

Automated neuromelanin imaging as a diagnostic biomarker for Parkinson's disease.

We aimed to analyze the diagnostic accuracy of an automated segmentation and quantification method of the SNc and locus coeruleus (LC) volumes based on neuromelanin (NM)‐sensitive MRI (NM‐MRI) in patients with idiopathic (iPD) and monogenic (iPD) Parkinsons disease (PD).

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.