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An example from the dataset. Figure 1a shows the bright-field image and 1b its corresponding SYTOX Green image. Figure 1c shows the binary mask and 1d the relabelled RGB mask. Best viewed in color. 

An example from the dataset. Figure 1a shows the bright-field image and 1b its corresponding SYTOX Green image. Figure 1c shows the binary mask and 1d the relabelled RGB mask. Best viewed in color. 

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Conference Paper
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Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live/dead assay dataset from the Broad Bioimage Benchmark Collection. These architectures include a standar...

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... dataset employed was the C. elegans live/dead assay, version 1, available from the Broad Bioimage Benchmark Collection (BBBC) [20]. This dataset consists of 97 16-bit Figure 1a shows the bright-field image and 1b its corresponding SYTOX Green image. Figure 1c shows the binary mask and 1d the relabelled RGB mask. ...
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... dataset consists of 97 16-bit Figure 1a shows the bright-field image and 1b its corresponding SYTOX Green image. Figure 1c shows the binary mask and 1d the relabelled RGB mask. Best viewed in color. ...
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... can, in some cases, make it difficult to observe the class of a worm. One such example can be seen in Figure 1a. Uneven illumination can be observed in both the bright-field and SYTOX images. ...
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... are two sets of ground truth labels, available sepa- rately from the BBBC website. The first contains 100 binary segmentation masks that shows the location of all the worms in each image (example in Figure 1c). This is one of the main ground truth labellings that will be used in experiments and will henceforth be referred to as the binary masks. ...
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... SYTOX images, such as Figure 1b, were thresholded in order to detect the highlighted worms. The single worm segmentations that correspond to highlighted worms in the SYTOX images were then relabelled as belonging to the dead class. ...
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... trend can be seen more clearly by ordering the models based on the size of their contextual window. The model with the smallest contextual window is model B (FCN-ND), followed by model C (FCN-1D), then model A (SCN-PT) and then lastly, the model with the largest contextual window is model D (FCN- 3D). The same order can be observed by placing the pixel-level classification metrics in ascending order. ...

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