Cell Segmentation Using a Similarity Interface With a Multi-Task Convolutional Neural Network.

Even though convolutional neural networks (CNN) have been used for cell segmentation, they require pixel-level ground truth annotations. This paper proposes a multitask learning algorithm for cell detection and segmentation using CNNs. We use dot annotations placed inside each cell indicating approximate cell centroids to create training datasets for the detection and segmentation tasks. The segmentation task is used to map the input image to foreground versus background regions, whereas the detection task is used to predict the centroids of the cells. Our multitask model shares convolutional layers between the two tasks, while having task-specific output layers. Learning two tasks simultaneously reduces the risks of overfitting and also helps in separating overlapping cells better. We also introduce a similarity interface (SI) that can be integrated with our multitask network to allow easy adaptation between domains, and to compensate for the variability in contrast and texture of cells seen in microscopy images. The SI comprises an unsupervised first layer in combination with a neighborhood similarity layer (NSL). A layer of logistic sigmoid functions is used as an unsupervised first layer to separate clustered image patches from each other. The NSL transforms its input feature map at a given pixel by computing its similarity to the surrounding neighborhood. Our proposed method achieves higher/comparable detection and segmentation scores as compared to recent state-of-the-art methods with significantly reduced effort for generating training data.