Segmenting Microscopy Images of Multi-Well Plates Based on Image Contrast

Abstract

Image segmentation is a key process in analyzing biological images. However, it is difficult to detect the differences between foreground and background when the image is unevenly illuminated. The unambiguous segmenting of multi-well plate microscopy images with various uneven illuminations is a challenging problem. Currently, no publicly available method adequately solves these various problems in bright-field multi-well plate images. Here, we propose a new method based on contrast values which removes the need for illumination correction. The presented method is effective enough to distinguish foreground and therefore a model organism (Caenorhabditis elegans) from an unevenly illuminated microscope image. In addition, the method also can solve a variety of problems caused by different uneven illumination scenarios. By applying this methodology across a wide range of multi-well plate microscopy images, we show that our approach can consistently analyze images with uneven illuminations with unparalleled accuracy and successfully solve various problems associated with uneven illumination. It can be used to process the microscopy images captured from multi-well plates and detect experimental subjects from an unevenly illuminated background.

Small flexible automated system for monitoring Caenorhabditis elegans lifespan based on active vision and image processing techniques

“…The correlation was performed for wild-type strain N2 strain and for mutants CF1038, daf-16 (mu86) (short-lived strain) and CB1370, daf-2 (e1370) (long-lived strain). In addition, the anti-diabetic drug metformin, which has been reported to increase C. elegans lifespan, was included in the validation study 36 – 38 .…”

“…Metformin is a widely used first-line drug for treatment of type 2 diabetes and has been shown to extend lifespan. Recently, it was shown that metformin increases C. elegans lifespan via the lysosomal pathway 36 , 43 .…”

Active backlight for automating visual monitoring: An analysis of a lighting control technique for Caenorhabditis elegans cultured on standard Petri plates

“…Usually a threshold is tuned manually during the assay. However, automatic adaptive methods are used by, for example, [18–20], and adaptive Gaussian is also employed [21], as is Otsu’s method [22, 23], which is a clustering-based image and a method based on contrast [24]. There are also graph methods [25, 26] or background modelling [27].…”

Development and Evaluation of New Methods for Automating Experiments with C. Elegans Based on Active Vision

“…The correlation was performed for wild-type strain N2 strain and for mutants CF1038, daf-16 (mu86) (short-lived strain) and CB1370, daf-2 (e1370) (long-lived strain). In addition, the anti-diabetic drug metformin, which has been reported to increase C. elegans lifespan, was included in the validation study [11], [24], [61].…”

“…Usually a threshold is tuned manually during the assay. However, automatic adaptive methods are used by, for example, [18]- [20], and adaptive Gaussian is also employed [21], as is Otsu's method [22], [23], which is a clusteringbased image and a method based on contrast [24]. There are also graph methods [25], [26] or background modelling [27].…”