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CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data

We present a method for foreground segmentation of yeast cells in the presence of high-noise induced by intentional low illumination, where traditional approaches (e.g., threshold-based methods, specialized cell-segmentation methods) fail. To deal with these harsh conditions, we use a fully-convolutional semantic segmentation network based on the SegNet architecture. Our model is capable of segmenting patches extracted from yeast live-cell experiments with a mIOU score of 0.71 on unseen patches drawn from independent experiments. Further, we show that simultaneous multi-modal observations of bio-fluorescent markers can result in better segmentation performance than the DIC channel alone.