Automatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networks

Computer assisted image acquisition techniques, including confocal microscopy, require efficient tools for an automatic sorting of vast amount of generated image data. The complexity of the classification process, absence of adequate tools, and insufficient amount of reference data has made the automated processing of images challenging. Mastering of this issue would allow implementation of statistical analysis in research areas such as in research on formation of t-tubules in cardiac myocytes. We developed a system aimed at automatic assessment of cardiomyocyte development stages (SAACS). The system classifies confocal images of cardiomyocytes with fluorescent dye stained sarcolemma. We based SAACS on a densely connected convolutional network (DenseNet) topology. We created a set of labelled source images, proposed an appropriate data augmentation technique and designed a class probability graph. We showed that the DenseNet topology, in combination with the augmentation technique is suitable for the given task, and that high-resolution images are instrumental for image categorization. SAACS, in combination with the automatic high-throughput confocal imaging, will allow application of statistical analysis in the research of the tubular system development or remodelling and loss.


Introduction
The cardiac muscle cells, cardiomyocytes, contract to propel blood flow [1]. In adult heart, ventricular myocytes contain a system of membraneous transversal tubules (t-tubules) continual with the sarcolemmal membrane [2]. T-tubules form during postnatal growth and maturation of cardiac myocytes and are prone to remodelling under physiological or pathological cardiac hypertrophy [3]. As the integrity of the tubular system is essential for the correct function of adult cardiac myocytes, understanding of their formation, loss and remodelling, and a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 factors influencing these processes is paramount for both the study of excitation-contraction coupling, as well as for the development of mature human induced pluripotent stem cell derived cardiomyocytes with correct phenotype [4].
Several developmental stages of t-tubule formation were identified in confocal microscopy images of fluorescently labelled sarcolemma of cardiomyocytes from growing rat hearts; however, high variability among myocytes of the same heart was observed and therefore the individual stages could not be simply assigned to specific period of heart development [5]. An alternative to quantification of t-tubules in single myocytes could be application of statistical methods on a large population of cardiomyocytes. This statistical evaluation would allow assessment of the t-tubule development, loss, and remodelling in different age groups or under different conditions. Current microscopy technologies, including confocal microscopy, enable automatic collection of large amounts of images; thus, allowing implementation of the statistical evaluation of t-tubule formation.
Different sampling modes are used in cardiomyocyte research while taking microscopy images. In one, the expert identifies and manually localizes individual myocytes in a field of view. The marked areas are then scanned [6]. Alternatively, signals are recorded at low spatial resolution and measured from the whole field of view. All obtained images may be later manually processed by the expert [7]. Both alternatives are suitable when a small number of target objects should be captured, e.g. when recording functional data from live cells, where several measurements are made from the same cell or culture plate well. Statistical analysis of morphological features, on the other hand, requires a high number of images of various individually recorded cells. These images can be easily obtained from the whole culture well or microscope slide of fixed cells using mosaic scanning functionality of modern confocal microscopes. Unfortunately, only a part of such created dataset contains high quality images of whole and healthy myocytes. The rest of the dataset consists of empty images, images of dead cardiomyocytes, out-of-focus images and images of cell fragments. Thus, before the data can be statistically analysed, the proper images in the datasets must be identified and sorted.
So far, when attempting to quantify the t-tubule complexity in cardiac myocytes, expert needs to identify healthy myocytes in the sample and record high quality images, which are then quantified using 2D spatial Fourier transform [8][9][10][11] or stereological analysis [5]. To statistically analyse the whole population, the expert would need to assign the t-tubule development stage to each observed image of a cardiomyocyte according to a complex set of object features and classification rules. Such an approach is impractical and prone to subjective errors. Considering the amount of data required for the statistical analysis, automatic classification of the images would be more appropriate. As the cardiomyocytes are objects of high complexity and show substantial morphological variability, the automatic classification is hindered by lack of implemented methods.
The automatic classification of images is a typical computer vision task known as generic object categorization [12]. The state-of-the-art image categorization systems rely on deep convolutional networks (deep ConvNets) [13]. Deep ConvNets naturally integrate feature extraction and classification into one compact unit. Before their utilization, they must be trained and evaluated on sets of labelled samples. Key factors influencing performance of a trained deep ConvNet-based image categorization system are the quality of the used training set and a learning capacity of the network.
The quality of a training set is mainly influenced by selection of training samples, their correct categorization, and the total number of samples of each category in the training set [14]. A class balance issue must be also considered whenever class-sensitive learning method is used [15]. In microscopy image analysis, a limited amount of source data is usually available 15- [ [16][17][18] which is also the case of confocal microscopy images of the cardiomyocytes. The lack of data limits sample selection and does not allow creation of a sufficiently large and representative training set. To overcome these concerns, data augmentation techniques, such as image translations, horizontal reflections [17], or rotations [16,18] are typically applied on inadequate sets. The representativeness of the sets can be further improved by using images independently categorized by several experts.
The capacity of a deep ConvNet is predetermined by its topology. A typical topology of the early deep ConvNets is as follows. The first levels of a network consist of convolutional layers, typically complemented with rectified linear unit activation functions, and of several pooling layers [13]. Subsequent levels usually contain fully connected layers including a dropout regularization technique [19][20][21]. The network is typically closed by a classifier employing a softmax function. The layers in the network are arranged in a feed-forward manner and the capacity of the network can be increased by increasing the number of layers (a depth of the network) [13]. Such a topology, however, does not allow construction of very deep networks due to a vanishing gradient problem [22]. Thus, deep ConvNets based on this topology are appropriate for problems with a relatively simple discriminability of target objects, e.g. for human epithelial-2 cell image classification [16], for classification of glioblastoma multiform and low-grade glioma [17], or for classification of red blood cells in sickle cell anemia [23]. This topology is, however, inappropriate for the classification of cardiomyocyte images due the complexity of the cardiomyocytes.
Modern, more complex deep ConvNet topologies overcome the vanishing gradient problem to a large extent; hence, they allow construction of very deep networks with great learning capacities. The new topologies control the capacity by varying width or depth of networks [21]. Enlarging a deep ConvNet capacity through increasing its width is used e.g. in GoogLe-Net [24,25], where several sub-networks are connected in parallel at various levels of the network. The current trends converge towards increased number of layers (the network depth), while retaining the data processing linearity. Topologies, such as Highway Networks [26], Residual Networks [27][28][29], Deep Pyramidal Residual Networks [30], Densely Connected Convolutional Networks (DenseNets) [31] and Cross-Layer Neurons Networks [32], fall into this category. All these topologies show very good classification performance even on datasets with high intraclass variability.
Given the progress in computer vision in last years, we decided to develop a system for automatic assessment of cardiomyocyte development stages (SAACS) from confocal images. SAACS is aimed to identify and classify whole and healthy myocytes in a set of images obtained while scanning the whole culture well or microscope slide of fixed cells, where the images are classified according to the developmental stages of individual cardiomyocytes. Considering the complexity of the cardiomyocytes and the limited number of images available for the forming of the training set, we based SAACS on the DenseNet topology. As the other modern deep ConvNet topologies, DenseNet topology alleviates the vanishing-gradient problem and it allows creation of networks with high learning capacity. Furthermore, according to [31], networks of DenseNet topology are proved to be robust against overfitting on tasks with small training sets. For a further improvement of the training process, we designed an efficient data augmentation technique that improved classification performance of SAACS. We show that SAACS trained on the augmented dataset, consisting of fifteen source images of each class only, was able to identify the whole and healthy myocytes, and differentiate among five cardiomyocyte development stages. In addition, we analysed the influence of image resolution on SAACS performance and implemented a class probability graph to simplify the expert evaluation of SAACS performance.

Isolated cardiomyocytes
Ethical statement. Animals were housed and treated according to the European directive for the protection of animals used for scientific purposes (2010/63/EU) and with the Laboratory Animals Act No. 377/2012 and the Decree 436/2012 Z.z. SR. All procedures were approved by the State Veterinary and Food Administration of the Slovak Republic (3514/14-221) and by the Ethical committee of the Institute of Molecular Physiology and Genetics, Slovak Academy of Sciences. Adult males and pregnant females were from Dobra Voda, Slovakia.
Confocal microscopy. Sarcolemma of isolated cardiomyocytes was stained using lipophilic membrane probes (di-8-ANEPPS or FM4-64, Molecular probes, Oregon, US). Images were obtained by the Leica TCS SP2 AOBS confocal microscope (Leica Microsystems, Germany) equipped with HCX PL APO CS 63x/1.2 NA water immersion objective. Fluorophores were excited by 488 nm and combined 496 and 514 nm laser light. Fluorescence emission was collected from 520 to 800 or 620 nm to 760 nm windows for di-8-ANEPPS or FM 4-64, respectively. Images were recorded in x-y mode with 58 nm to 116 nm per pixel (px) with 4× frame averaging to increase the signal-to-noise ratio, with the confocal aperture set to 1 Airy unit.

Training and evaluation sets
Classification of object images. The expert identified the live and healthy cardiomyocytes in images and assigned them into 5 development stages. The least developed cardiomyocytes were considered to be at stage 1 while the most developed ones were assigned stage 5 (Table 1). We used the stages to define classes of object images where the stages 1 to 5 correspond to classes 1 to 5, respectively. The rest of images in the collection (low quality images or images which do not contain whole and healthy myocytes) belong to the class 0.
Cardiomyocytes with no membrane invaginations were categorized as stage 1. Stage 2 was characterized by presence of short (~2 μm), regularly spaced, perpendicular invaginations of the membrane and/or individual long (>10 μm) tubules. In stage 3, a web of longer tubules (>10 μm), both transverse and longitudinal, was present. A complex system of transversal tubules with manifest presence of longer (>2 μm) longitudinal tubules and frequent areas devoid of tubules was classified as stage 4. Stage 5, present in adult cardiomyocytes, was distinguished by a complex system of transversal tubules, with sparse longitudinal structures spanning usually not more than 1 to 2 sarcomeres (~2 μm to~4 μm) filling the whole area of the cell image except nuclei. Examples of development stage categories are shown in Fig 1. Source images. Confocal microscopy produces high-resolution monochromatic images of optional dimensions. In our case, images 1024 × 1024 px were considered and each image contained not more than one target object (cardiomyocyte). An example of original source image and of its enhancement for better representation in the article are shown in Fig 2. As the images were recorded in the traditional way (manually by an expert), a very limited number of appropriate images was available to form a training and an evaluation set. Our dataset consisted of the images analysed in [5], to which we added 12 images of class 5, and all class 0 images. In total, 18 images of class 1, 23 images of class 2, 22 images of class 3, 22 images of class 4, and 26 images of class 5 were available. Compared to classes 1 to 5, the number of available class 0 images was not limiting. Our collection of source images was imbalanced, which might compromise training of deep ConvNets [33]. To deal with the imbalanced data issue, a random under-sampling method [34] was used to form a class-balanced set of source images.
To create the set, we randomly selected 15 images of each class from the original image collection. The class-balanced set was used as the base for creating the training set. The remaining (unselected) images were used as source images for the evaluation set. In addition, 15 and 9 source images of class 0 were allocated to the training and the evaluation set, respectively. The data are available at [35]. Data augmentation. The small number of source images did not allow efficient training (90 images) or credible evaluation (46 images) of any deep ConvNet [13]. To overcome this issue, data augmentation techniques were used. To prevent alteration of t-tubule transversal pattern, which we consider important for correct classification, non-destructive augmentation techniques were chosen. Specifically, the source images were horizontally flipped, and both the flipped and the original images were rotated by an angle φ 2 {0, Δφ, 2Δφ, . . ., 2π}, where Dφ ¼ 2p 144 (first method). Additionally, to enhance the recognition of the objects of interest, we have developed a second augmentation technique, which applies the first method on an extended set of source images. The extended set consisted of the original source images and manually modified images. The modified images were based on cut-outs of target objects (cardiomyocytes). From each original source image (Fig 3(a)), the target object was extracted; and from each extraction, two new source images were created. In both images, the target object was placed on a neutral or noisy background, at a random position within the image. While the first new image consisted only of the target object and the background (Fig 3(b)), the second one (Fig 3(c)) was enhanced with non-target objects that naturally occur in the confocal images. The non-target objects were placed randomly in the image, but they did not overlap with the target object. In this way, the source images of classes 1 to 5 were extended. Class 0 images were extended by 30 new unique source images. Thus, the set augmented using this technique was three times larger than the set produced by the first data augmentation technique.
Set up of training and evaluation sets. We hypothesized that due to the small size (~0.1 μm to~0.3 μm in diameter) and dense spacing (~1.8 μm to~2.0 μm) of t-tubules, highresolution images are key for the efficient assessment of the cardiomyocyte development stages. The available source images were of dimensions 1024 × 1024 px, with resolution 58 nm to 116 nm per px. To assess the importance of image resolution on SAACS classification performance, we created low-resolution sets of source images. The original source images were resized to 512 × 512 px using a resize method with bicubic resampling filter in python imaging library 5.0.0, resulting in resolution of 116 nm to 232 nm per px.
We Dense block. A DB consists of several layers connected in a dense pattern where each layer takes all preceding feature-maps as its input (Fig 4). The layers are connected using concatenation and transformed using a non-linear transformation. Let us consider a n-th DB that is built in a DenseNet of L layers. A non-linear transformation performed within this DB, placed at the ℓ-th level within the network, produces feature maps x ℓ that are given as where H ℓ (�) is the ℓ-th non-linear transformation, ½x i n ; x i n þ1 ; . . . ; x 'À 1 � refers to the concatenation of the feature maps x produced in layers i n , . . ., (ℓ − 1). Note that x i n are feature maps fed into the input of the n-th DB. The input of the DB is placed at the i n -th level of the network, i n < ℓ � o n , and o n is a level at which the output of the n-th DB is placed.
The ℓ-th non-linear transformation H ℓ (�) is a composite function of several consecutive operations. We used a basic and a bottleneck version of the composite function [31]. The basic version consists of three operations: batch normalization (BN) [36], followed by a rectified linear unit (ReLU), followed by a convolution (Conv) [13]. Using a short notation, this version of the function can be written as BN-ReLU- Conv(h × w, f, s), where s is stride of convolutional filters, f is number of the filters, and h and w are their height and width, respectively. The  × w, s, f, d) and DBb(h × w, s, f, d), respectively.
Transition layer. Let us consider a TL connected at the output of the n-th DB (i.e. the layer is placed at the (o n + 1)-th level of the network). Feature maps produced by this layer are given as  produced by the (o n + 1)-th TL is given as f o n þ1 ¼ bym n c, where θ is a compression factor, and y 2 ½m À 1 n ; 1�. SAACS structure. SAACS is a DenseNet that performs feature extraction and classification on size-normalized object images. We designed and implemented two variants of SAACS (i.e. two DenseNets). One variant (DenseNet-512) was aimed at object images of dimensions 512 × 512 px and the second one (DenseNet-1024) at object images of dimensions 1024 × 1024 px. As the object images are monochromatic, numbers of input channels of the networks (depths of the network inputs) are one.
Both the DenseNet-512 (Table 2) and the DenseNet-1024 (Table 3) h  7  3  3  -3  -3  -3  -3  7  w  7  3  3  -3  -3  -3  -3 7 - In the first row, the used building components are listed with respect to their placement in the network (the first block is the leftmost one); where DBa and DBb are the basic and the bottleneck versions of the dense blocks; MPL is the max pooling layer; TL is the transition layer, GAP denotes the global average pooling, and C is used for a classifier that consists of one fully connected layer followed by the softmax function. The parameters h and w are the height and weight of the filter kernel or of the pool; s is stride of the kernel of the pool; f is the number of filters at one convolution in the dense block; and d is the number of layers in the dense block. https://doi.org/10.1371/journal.pone.0216720.t002 The meaning of the symbols and variables is explained in Table 2. https://doi.org/10.1371/journal.pone.0216720.t003

SAACS evaluation
For the SAACS performance evaluation, we used an average per-class accuracy (acc), a macroaveraging precision (pr M ), and a macro-averaging recall (re M ) [37]. These standard measures are sufficiently informative if the evaluation is performed on a set with non-overlapping classes.
As the growth of cardiomyocytes and t-tubules is a continuous process, an individual myocyte might possess a combination of features belonging to different neighbouring classes (i.e. the classes may overlap). It means that both the expert assessments and the SAACS classifications may vary, which negatively influences the information value of the performance measures. Thus, we designed a class probability graph for a more precise evaluation of the SAACS performance.
SAACS classifies object images presented on its input. For an object image, the softmax function produces class label probability scores. The class probability graph displays scores of each sample (object image) in an evaluation set as a bar of fixed height. Starting with class 0, the bar is coloured from the top down, respecting the order of the classes. We organized samples in the evaluation set with respect to source images, their rotation and flipping. The bars (samples) are divided into non-flipped and flipped columns.
Simply, Fig 6 depicts hypothetical evaluation results obtained on a set of 24 rotations (by Dφ ¼ 2p 23 ) of 6 source images, one of each class. The classes 0 to 5 are associated with black, red, yellow, green, light blue and dark blue colour, respectively. The source images are sorted in ascending order according to their class and displayed from top to bottom. The samples are sorted in ascending order according to their rotation angle φ (the angle in degrees is stated above the first row of bars).
In this example, all samples of the classes 0, 1 and 4 are correctly classified. The first 10 samples created from the source image 2 (class 2) were incorrectly classified as class 3. Similarly, the 7-th sample based on the source image 3 (class 3) was incorrectly classified as class 4. Such errors are questionable (classes 2 and 3, or classes 3 and 4 are neighbour classes), and should be reviewed by an expert. However, the incorrect assignment of the 1-st sample based on the source image 5 (class 5) is unjustifiable (classes 5 and 0 are not neighbours).

SAACS implementation
The DenseNet-512 and the DenseNet-1024 were trained using an ADAM optimizer [38]. The learning rate α was set up at 5 � 10 −4 , and the exponential decay rate for the first and Automatic assessment of the cardiomyocyte development stages from microscopy images using deep ConvNets second moment estimates β 1 and β 2 were setup at 0.95 and 0.999, respectively. The optimizer and setting of the hyperparameters were determined based on a pilot study. A cross entropy function was minimized. The DenseNet-512 was trained on the sets T-512-A and T-512-B. The DenseNet-1024 was trained on the sets T-1024-A and T-1024-B. We used batches of size 9 and 3 samples while training the DenseNet-512 and the DenseNet-1024, respectively. Size of the batches was given by the capabilities of a used hardware. To ensure comparability of the results, the networks were trained on the same total number of samples (i.e. the same total number of iterations was carried out). Specifically, the networks were trained for 15 epochs on T-512-A and T-1024-A, and for 5 epochs on T-512-B and T-1024-B.
To keep comparability of the results, the DenseNet-512 trained on T-512-A or T-512-B was evaluated on the set E-512-A. Similarly, the DenseNet-1024 trained on T-1024-A or T-1024-B was evaluated on the set E-1024-A. Performance of the trained networks was assessed using acc, pr M and re M . Further, class probability graphs were used for expert assessment of SAACS performance.
We trained and evaluated the networks on a computer with 64 GB RAM, Ryzen 5 1600 CPU, and a graphic card nVidia GTX-1050 Ti with 4 GB GDDR5. The batches were formed from a shuffled queue of training samples. The training and evaluation of the networks was carried out in a python 3.6.4 environment using Tensorflow 1.5 [39]. Python implementations of the networks, of training and evaluation routines, likewise of a code which delineates class probability graphs are provided in a supplementary file S1 File.

Results
The ability of SAACS to assess cardiomyocyte development stages from confocal microscopy images varied with respect to the composition of training sets and with respect to the resolution of object images ( Table 4) For each combination of network and training set, we obtained one class probability graph. The graphs obtained for the DenseNet-512 (Figs 7 and 8) showed incapability of this network The DenseNet-512 and the DenseNet-1024 were trained on sets created using the first (T-512-A and T-1024-A) and the second data augmentation technique (T-512-B and T-1024-B). The trained networks were evaluated using average per-class accuracy (acc), macro-averaging precision (pr M ), and macro-averaging recall (re M ). To provide comparable results, the same total number of iterations was carried out while training the networks. https://doi.org/10.1371/journal.pone.0216720.t004 Automatic assessment of the cardiomyocyte development stages from microscopy images using deep ConvNets to recognize samples of the classes 0 and 1. The DenseNet-512 trained on T-512-A labelled most of the samples as class 2 or 3, even if they belonged to the class 5 (Fig 7). The same network trained on T-512-B tended to classify the samples as the classes 3 or 5. In this case, incorrectly classified samples were more often assigned to neighbour classes (Fig 8). Compared to the DenseNet-512, the DenseNet-1024 trained on T-1024-A was more accurate in classification of samples from classes 1 to 5 (Fig 9). Moreover, incorrectly classified samples were mostly assigned to the neighbour classes. Unfortunately, the DenseNet-1024 trained on T-1024-A was unable to recognize samples of the class 0. Finally, the DenseNet-1024 trained on T-1024-B showed similar performance considering the classes 1 to 5 but it also correctly identified samples of the class 0 (Fig 10).

Discussion
The automatic assessment of cardiomyocyte development stages in confocal microscopy images is a complex task due to the high variability of the cardiomyocytes in the images. We proved the DenseNet topology is suitable for this task. Considering the evaluation results (Table 4), we find the DenseNet-1024 trained on T-1024-B to be ideal for SAACS implementation, as it nearly correctly classified samples of all classes (Fig 10). Although the classes 1 to 4 still contained a number of wrongly classified samples, the errors were acceptable, as a typical Automatic assessment of the cardiomyocyte development stages from microscopy images using deep ConvNets error was an assignment of the sample into a neighbouring class. We expect that further improvement of SAACS performance will be possible once SAACS is applied in practice, as SAACS can be retrained on its outputs in semi-supervised manner. SAACS based on DenseNet-1024, in combination with the automatic high-throughput confocal imaging, opens the possibility to use statistical analysis of large cell populations in the research of the tubular system development or remodelling and loss. This approach has several advantages compared to the conventional methods such as 2D spatial Fourier transform [8][9][10][11] and stereological analysis [5]. The 2D spatial Fourier transform is efficient only for late development stages, where discerned patterns of tubules running perpendicular to the long axis of the cell are formed. The stereological analysis can be theoretically applied over a whole range of developmental stages, but it is impractical due to the necessity to analyse hundreds of images by experts in high detail. Our approach allowed studying of the development process in its full range, from the earliest to the latest developmental stages and needed only 90 images assessed by the expert.
The results in Table 4 further show that, compared to the conventional data augmentation techniques (T-512-A and T-1024-A), the augmentation of training sets using manually modified images (T-512-B and T-1024-B) improves performance of both SAACS versions (Dense-Net-512 and DenseNet-1024). In our experiments, two new images were created from each Automatic assessment of the cardiomyocyte development stages from microscopy images using deep ConvNets source image. As expected, such groups of images emphasize differences between target (Fig  3b)) and non-target objects (Fig 3c)). This approach allowed us to improve correct recognition of class 0 samples which is especially evident in the DenseNet-1024 results (Figs 9 and 10). As this data augmentation technique proved to be highly efficient, we recommend its utilization whenever only a limited set of source images is available.
We observed a marked difference in performance between the DenseNet-512 and the Den-seNet-1024 (Table 4), where the DenseNet-1024 showed better performance. The obtained results confirm our hypothesis that the high-resolution images are essential for proper operation of SAACS. We find the explanation for the better performance of SAACS with high-resolution images in the fine pattern of t-tubules, which is one of the most important distinctive characteristics of cardiomyocyte development stages. The need for high-resolution microscopy images was also observed in a study that used a deep ConvNet in digital histopathology images of resolution 224 × 224 px to classify glioblastoma multiform and low-grade glioma [17]. We expect that fine patterns are important in many similar tasks within biology. As the fine patterns disappear while down-sampling the images, high-resolution object images must be used to ensure optimal performance of image categorization systems in cases where the fine pattern is distinctive, even accounting for the higher computational costs in processing large images. We feel the need to highlight this fact since low-resolution images are preferred in practice for their low computational cost. For example, 78 × 78 px object images were used for HEp-2 cell image classification [16], and for classification of red blood cells in sickle cell anemia [23]. The basic prerequisite for the successful use of low-resolution images for object categorization is the existence of discriminatory features in these images. In the cases of the HEp-2 cell [16] and red blood cells images [23], expressive patterns and distorted morphologies, respectively, were the adequate features for the given classification tasks.

Conclusion
We developed the system for automatic assessment of cardiomyocyte development stages in confocal microscopy images (SAACS). In combination with automatic high-throughput confocal imaging, SAACS will allow the use of statistical analysis for the research on the tubular system development, remodelling and loss. Classification of large cardiomyocyte populations and studying the class distribution in populations under different conditions may become a new approach in the study of structural remodelling associated with physiological or pathological processes, as well as in the development of protocols for the induction of maturation in induced pluripotent cell derived cardiomyocytes. Automatic assessment of the cardiomyocyte development stages from microscopy images using deep ConvNets The development of SAACS was complicated by: a) high variability in the cardiomyocyte shape and structure, and b) lack of source images. To face these issues, we based SAACS on the DenseNet topology and we modified the standard data augmentation technique with manually adjusting source images. As both issues are common in biology, we recommend using the DenseNet and the augmentation technique in biological applications, such as morphologybased cell sorting, identification of regions of interest or projects that today require Citizen science approach. DenseNets should be used instead of the commonly used early deep ConvNets topologies [16,17,23] especially if target objects exhibit high variability in their shapes and structures. We also advise to use the manually modified images whenever only a limited collection of source images is available, especially if there is problem with recognition of the object of interest. Considering the fact that the DenseNets can process any type of images (monochromatic, colour, hyperspectral, etc.), DenseNets in combination with the data augmentation technique are ideal means for creation of image categorization systems for various tasks in biology.
Supporting information S1 File. Implementation of the SAACS in python. Codes that were used for training and evaluation of the DenseNet-512 and DenseNet-1024, including code which delineates class probability graphs. (RAR)