Localisation microscopy with quantum dots using non-negative matrix factorisation

Mandula, Ondřej; Šestak, Ivana; Heintzmann, Rainer; Williams, Christopher K. I.

doi:10.1364/oe.22.024594

Cited by 15 publications

(15 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The precision and the recall can be calculated as Precision = TP TP+FP , Recall = FP FP+FN respectively, where TP, FN, and FP refer to the number of true positives, false negatives, and false positives respectively. Given the pixel locations where a bacterium has been annotated by the clinician, we defined a disk of radius r = 10 pixels [28], and we consider that any detection that is present within the disk as a match (TP); any detection outside any of the disks as FP; and any clinician's annotation that does not match with any of the algorithm detection as FN.…”

Section: Algorithm Evaluationmentioning

confidence: 99%

Patch-Based Sparse Representation For Bacterial Detection

Eldaly

Altmann

Akram

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

View full text Add to dashboard Cite

In this paper, we propose an unsupervised approach for bacterial detection in optical endomicroscopy images. This approach splits each image into a set of overlapping patches and assumes that observed intensities are linear combinations of the actual intensity values associated with background image structures, corrupted by additive Gaussian noise and potentially by a sparse outlier term modelling anomalies (which are considered to be candidate bacteria). The actual intensity term representing background structures is modelled as a linear combination of a few atoms drawn from a dictionary which is learned from bacteria-free data and then fixed while analyzing new images. The bacteria detection task is formulated as a minimization problem and an alternating direction method of multipliers (ADMM) is then used to estimate the unknown parameters. Simulations conducted using two ex vivo lung datasets show good detection and correlation performance between bacteria counts identified by a trained clinician and those of the proposed method.

show abstract

Section: Algorithm Evaluationmentioning

confidence: 99%

Patch-Based Sparse Representation For Bacterial Detection

Eldaly

Altmann

Akram

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

View full text Add to dashboard Cite

show abstract

“…Given the locations of pixels where an object (bacterium or cell) has been detected and the annotations by the clinician, we draw a disk of radius r around the annotations, and if a detection exists within the disk then it is declared to be a match as in [14].…”

Section: Performance Metricmentioning

confidence: 99%

Estimating Bacterial and Cellular Load in FCFM Imaging

et al. 2018

View full text Add to dashboard Cite

Abstract:We address the task of estimating bacterial and cellular load in the human distal lung with fibered confocal fluorescence microscopy (FCFM). In pulmonary FCFM some cells can display autofluorescence, and they appear as disc like objects in the FCFM images, whereas bacteria, although not autofluorescent, appear as bright blinking dots when exposed to a targeted smartprobe. Estimating bacterial and cellular load becomes a challenging task due to the presence of background from autofluorescent human lung tissues, i.e., elastin, and imaging artifacts from motion etc. We create a database of annotated images for both these tasks where bacteria and cells were annotated, and use these databases for supervised learning. We extract image patches around each pixel as features, and train a classifier to predict if a bacterium or cell is present at that pixel. We apply our approach on two datasets for detecting bacteria and cells respectively. For the bacteria dataset, we show that the estimated bacterial load increases after introducing the targeted smartprobe in the presence of bacteria. For the cell dataset, we show that the estimated cellular load agrees with a clinician's assessment.

show abstract

“…Given the locations of pixels where a bacterium has been detected and the annotations by the clinician, we draw a disk of radius r around the annotations, and if a detection exists within the disk then it is declared to be a match as in [7].…”

Section: P = Tp Tp + Fpmentioning

confidence: 99%

Estimating Bacterial Load in FCFM Imaging

Seth

Akram

Dhaliwal

et al. 2017

Communications in Computer and Information Science

Self Cite

View full text Add to dashboard Cite

We address the task of detecting bacteria and estimating bacterial load in the human distal lung with fibered confocal fluorescence microscopy (FCFM) and a targeted smartprobe. Bacteria appear as bright dots in the image when exposed to a smartprobe, but they are often difficult to detect due to the presence of background autofluorescence inherent to human lungs. In this study, we create a database of annotated image frames where a clinician has labelled bacteria, and use this database for supervised learning to build a suitable bacterial load estimation software.

show abstract

Localisation microscopy with quantum dots using non-negative matrix factorisation

Cited by 15 publications

References 19 publications

Patch-Based Sparse Representation For Bacterial Detection

Patch-Based Sparse Representation For Bacterial Detection

Estimating Bacterial and Cellular Load in FCFM Imaging

Estimating Bacterial Load in FCFM Imaging

Contact Info

Product

Resources

About