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Plasmonic Nanoparticle-based Digital Cytometry to Quantify MUC16 Binding on the Surface of Leukocytes in Ovarian Cancer

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Sierra Research’s team has developed the algorithms that lead to the data of the following article: https://pubs.acs.org/doi/abs/10.1021/acssensors.0c00567 Plasmonic nanoparticles conjugated with anti-CA125 antibody were used to quantify MUC16 binding on leukocytes of ovarian cancer patients. Results show an elevated presence of MUC16 on ovarian cnacer patients with respect to healthy controls. Further, the assay was…

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Computer Aided Detection of Pulmonary Embolism Using Multi-slice Multi-axial Segmentation

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Can AI detect pulmonary emboli from CT scans? In a recent publication Sierra Research has teamed up with Universidad de Alicante to develop AI models for the detection of pulmonary emboli. We ranked top on the cad-pe challenge! Paper: https://www.mdpi.com/2076-3417/10/8/2945 Abstract: Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the…

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H-EM

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H-EM: an algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry Sierra’s Researcher in collaboration with URJC and Madrid-MIT M+Vision Consortium have published a PlosOne paper on low-resolution cell analysis. You can find the whole article at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222265

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Localizing Image-based biomarker regression without training masks: a new approach to biomarker discovery

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Can artificial intelligence regression networks locate the regions of interest from whom they are obtaining their value? In this conference article, Sierra’s researchers explore such question. From Computer Tomography axial slices obtained at the level of the transversal aorta, and the areas of their pectoralis muscle and subcutaneous fat, we have been able to perform…

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Biomedical Image Processing with Containers and Deep Learning

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Data architecture, artificial intelligence, automated processing, containerization, and clusters orchestration ease the transition from data acquisition to insights in medium‐to‐large datasets. In this journal paper, Sierra’s researchers, in collaboration with PNP Research Corporation (MA, USA) and the Wellman Center for Photomedicine (Boston, MA, USA), discuss how to handle large microscopy datasets and process them automatically…

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On the Relevance of the Loss Function in the Agatston Score Regression from non-ECG Gated CT Scans

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Given a biomarker regression network, what is the effect of training with different cost functions? In this conference publications, Sierra’s researcher investigate the influence of training with L1, L2, L1 in logarithmic scale or L2 in logarithmic scale for the problem of Agatston score regression from CT volumetric images center at the heart. There is…

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Multi-Structure Segmentation from Partially Labeled Datasets.

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Can we generate a single multi-structure segmentation network from partially annotated datasets? In this conference paper Sierra’s researchers in conjunction with Brigham and Women’s Hospital answer such question, showing that a) it is equivalent to use a single unet and b) that adding convolutions on the skip connections further improve segmentation results. The full article…

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Automated Agatston Score Computation in non-ECG Gated CT Scans Using Deep Learning

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Introduction: The Agatston score is a well-established metric of cardiovascular disease related to clinical outcomes. It is computed from CT scans by a) measuring the volume and intensity of the atherosclerotic plaques and b) aggregating such information in an index. Objective: To generate a convolutional neural network that inputs a non-contrast chest CT scan and…