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Deep Learning for Denoising of Fluorescence Microscopy Images Tram-Anh Nguyen Guy Hagen and Jonathan Ventura George Mason University University of Colorado Colorado Springs Fairfax, VA Colorado Springs, CO [email protected] [email protected], [email protected] Abstract from microscopy images. These filtering techniques have limitations, which call for a more generalized solution. Fluorescence microscopy images are often taken at low Deep learning methods have been successful in restoring light and short exposure times to preserve the integrity corrupted images, as well as in other image processing tasks of cell samples. However, imaging under these condi- such as classification, segmentation, and object detection. A tions leads to severely degraded images with low sig- nal to noise ratios. To computationally restore these im- widely used deep learning architecture in image processing ages, we introduce novel loss functions to denoise mi- is a convolutional neural network (CNN). A CNN consists croscopy images. These loss functions will be folded of an input layer, hidden layers, and an output layer. Another into the CARE algorithm. The results produced by this popular network in image processing is an autoencoder. Typ- modification will be evaluated against traditional TV fil- ically, autoencoders are used for denoising and reducing the tering and NL means techniques. The modified model number of dimensions of input data (Xing et al. 2017). will also be compared against its CARE predecessor us- ing standard image quality metrics. Related Work Deep learning approaches to image deblurring may involve Introduction blind and non-blind image deconvolution. There are a wealth of studies devoted to the non-blind image deconvolution ap- Fluorescence microscopy is vital for understanding pro- proach, but these networks are limited, as they rely on in- cesses and structures at the cellular level. Because imaging formation about the non-blurry image beforehand. By con- at the cellular level under strong lighting conditions or long trast, blind deblurring models are more flexible since in- exposure times may damage the cell sample through pho- formation about the non-blurry image is not required for the totoxicity, fluorescence microscopy images need to be re- network to deblur an input image. stored. A safe way to image a cell is to use low light condi- In 2014, Xu et al. introduced a natural image deconvo- tions and/or low exposure times, which unfortunately lowers lution that is data-driven and does not rely on traditional the signal to noise ratio (Xing et al. 2017). assumptions. For example, generative models tend to as- sume that noise in an image is identically and independ- ently distributed, even if this assumption is not necessarily true. Instead, this CNN was trained on images that were not deblurred ahead of time and the network learned the decon- volution operation without requiring information about the original image. The main contribution of Xu et al. was devel- oping this deep convolutional neural network (DCNN) that consisted of two sub models—one for deconvolution and the other for denoising. The models perform inverse filtering us- ing large 1D kernels and the former sub model is pre-trained to mimic Weiner deconvolution (Xu et al. 2014). A major drawback of the previously discussed blind Figure 1: Fluorescence microscopy images taken of actin DCNN was that it failed if the original image was not blurry. under high light (left) and low light conditions (right) To address this shortcoming, Conti et.al introduced a con- volutional neural network that consisted of a regularization Noise in an image depends on a combination of factors, term in the cost function. This improved model was able including exposure time and physical experimental condi- to denoise a blurry image and maintain the quality of im- tions. In fluorescence microscopy, noise is typically de- ages that are not noisy. The major modifications made by scribed by a Poison-Gaussian model. There has been extens- Conti et al. were that they used single 2D convolutional lay- ive work done in image restoration through filtering noise ers rather than 1D kernels for deblurring. The regularized cost function was built using the results of a classification microscopy images. network trained to distinguish blurry and non-blurry images that had roughly 80 percent accuracy when evaluated(Conti, Research Questions and Hypothesis Minucci, and Derakhshan 2017). The questions we will address in this study are: More recently, Weigert et al. published a series of image restoration methods that succeeded in restoring seven im- 1. How well does the CARE network perform on our micro- ages of various organisms (i.e. planaria flatworm, fruit fly scopy data set? wings). The major contributions of this work include: gener- 2. How can we produce image restorations that are sharper ating training data without requiring manual labeling, replic- than those produced by CARE? ating live imaging for organisms in which live imaging had once been near-impossible, and restoring microscopy im- 3. How does the network perform when it is trained on one ages even when lighting conditions are reduced by 60-fold. kind of sample and tested on a different kind of sample? Weigert et al. demonstrated high quality results with restor- 4. Can we reliably restore live cell images? ations of images containing Tribolium castaneum (red flour We hypothesize that our improvement on the method of Wei- beetle) and Schmidtea mediterranea (a flatworm commonly gert et al. will restore microscopy images that are less blurry known as planaria). This Content-Aware Image Restoration and more detailed than the restorations of the original CARE (CARE) network is based on the U-Net network, which con- model. Thus, our model will be a more faithful solution sists of an encoder-decoder architecture. The only difference compared to the denoising approach of Weigert et al. between the CARE network and the U-net algorithm is that the former outputs a per-pixel Laplace distribution whereas Proposed Implementation the latter outputs one value per pixel. Although these res- toration methods are promising, each pair of image content We modified the loss function of the CARE network in and corruption requires a unique data set. Each model must hopes of producing restorations that are more faithful to be retrained for an image of that particular content and cor- ground truth images. Ultimately, the objective of this study ruption to be successfully restored (Weigert et al. 2017). is to restore microscopy images free of artifacts and without Modifying network architecture is a popular strategy to loss of fine details. achieve better performance. Another widely used approach to this goal is data augmentation. Data augmentation is Experimental Setup widely used for image classification(Paulin et al. 2014), as The data set we used to train the standard CARE model is applying random transformations to training data effectively High Low, which consists of over 400 fluorescent images of provides more data for a network to learn from. Not sur- actin and mitochondria, in addition to 170 images of dendra. prisingly, training with large data sets produces high-quality All images in the High Low data set were taken using an image restorations (Burger, Schuler, and Harmeling 2012). Olympus IX83 with 60X/1.3NA objective lens. The Andor Typical transformations for data augmentation include trans- Zyla CMOS camera was used to image cell organelles. lation, rotation, and scaling. Augmenting data is a manual For all of our experiments, we first observed the beha- process in which these image transformations are specified vior of the network and assessed the quality of the network by humans. To automate this process, Jain et al. developed without loss function modifications. These results were then an unsupervised learning procedure that generated training compared to results produced by training the CARE network samples using different noise models (Jain and Seung 2009). using our FFT and bandpass cost functions. These experi- Likewise, Hauberg et al. developed a learned augmentation ments include restorations of actin imaged at 1 millisecond scheme that outperforms manual augmentation of MNIST and 10 milliseconds, restorations of mitochondria imaged data when used as training for a multilayer perceptron and a at 1 millisecond, model mismatch experiments, and restora- CNN (Hauberg et al. 2016). tions of dendra imaged at 10 milliseconds. We use the term Lastly, altering the loss function is a viable strategy, model mismatch to indicate experiments in which images though this approach tends to be overlooked (Zhao et al. of one type of cell content are used for training while im- 2017). Typically, the mean absolute error and mean squared ages of another type of cell content are used for testing. For error loss functions are employed in image processing net- example, we used images of mitochondria as training data works (Zhao et al. 2017; Burger, Schuler, and Harmeling for the CARE algorithm, and subsequently tested the model 2012; Agostinelli, Anderson, and Lee 2013; Chen et al. using noisy images of actin. Our most recent experiments in- 2018). In 2017, Zhao et al. introduced a new loss function for volved restorations of dendra imaged at 10 milliseconds us- image restoration that combined the multi-scale SSIM (MS- ing dendra imaged at 10 and 400 milliseconds as the training SSIM) metric with L1 loss. Without changing network ar- set. The results of these experiments were evaluated using chitecture, Zhao et al. demonstrated that by using this mixed peak signal to noise (PSNR) and structural similarity (SSIM) loss function, their fully convolutional neural network out- image quality metrics. performed state-of-the-art networks on tasks such as joint To conduct our experiments, we used the default config- denoising and demosaicking (Zhao et al. 2017). Drawing in- urations of the standard CARE model.

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