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FIGURE 5 U in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 5 U-Net implementation. The architecture of the used convolutional neural network (CNN) is an implementation of U-Net. It consists of two parts: two 3×3 convolutions followed by 2×2 max pooling and two 3×3 convolutions followed by 2×2 upconvolutions. Dropout was added to avoid overfitting. As a final step a 1×1 convolution is applied, resulting in an output map with two classes.

FIGURE 6 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 6 Network performance evaluation. High true positive rate (TPR) and low false positive rate (FPR) values for training (blue) and testing data (red) indicate the network's high generalizability.

FIGURE 10 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 10 Application of pipeline for other insect species. The brain textures of various insect species can be very similar to those of ants, facilitating the prediction by the network even without pretraining on specific insect brain scans. (a) Raw image of wasp head (original 1000 × 1000 px) and (b) its prediction without postprocessing (original 520 × 520 px), indicating satisfactory identification of the borders of the brain area. (c) 2D image of praying mantis head (520 × 520 px) and (d) the prediction of its brain area without postprocessing. Even though the network overpredicts some small pixel islands, it excludes from its prediction areas of the muscles, fibers, and cuticle.

FIGURE 1 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 1 Segmentation pipeline overview. (a) Specimens are placed in iodine for staining for 2 weeks and then placed in small vials containing 99% ethanol to prevent them from moving during scanning. (b) The computed tomography (CT) scanner acquires successive X-ray images of the stepwise rotating specimen, and, using a user-defined reference image, automatically reconstructs them to produce orthogonal cross-section stacks that are used for the volume reconstruction of the specimen. (c) Volume rendering for future morphological studies is performed using Amira software. (d) Semiautomated segmentation of the brain volume of each scan (in orange) using the watershed method in Amira. (e) Schematic representation of the U-Net architecture used as the core of the pipeline for the development of a fully automated brain segmentation method. (f) The acquired brain images are used for training after preprocessing augmentation and manual creation of masks. (g) The network's prediction (in yellow) is postprocessed for smoothing out overpredicted areas (in red).

FIGURE 2 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 2 Exemplar images of full-body scans from different ant species. Three-dimensional (3D) reconstructed microcomputed tomography (micro-CT) image of (a) Acromyrmex versicolor and (b) Atta texana worker specimens, using volume rendering in Amira. (c) 2D micro-CT full body image of the Atta texana specimen (original 1000 × 1000 px). The brain area is the area with the most uniform pixel density within the whole body in its stained state, which makes it easy to recognize in most high-quality scans.

FIGURE 3 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 3 Example of semiautomated brain image segmentation. The brain area (in orange) of an Atta texana ant specimen was segmented using the watershed method in Amira; the 1000 × 1000 × 1000 px 3D image was manually postprocessed by smoothing and cropping oversegmented areas.

FIGURE 9 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 9 Prediction of ganglia in the thorax. As the tissue texture in the image is similar to that of the brain, the network accurately predicts other areas of nervous tissue in the organism. The pixel island detection step isolates the brain, but without this step neural tissue can be isolated.

FIGURE 8 3D in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 8 3D volume of ant brain reconstructed from 2D images (original 520 × 520 px) predicted by the algorithm. 3D reconstructed brain prediction of an Atta texana worker.

FIGURE 7 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 7 Pipeline performance demonstrated both for validation (top row) and testing (bottom row) sets. (a, d) Raw images of head of Acromyrmex versicolor and Carebara atoma ant specimens, cropped along the x-y axes. The manually segmented brain areas are indicated in blue. (b, e) Network predictions before postprocessing (in yellow). Areas in yellow dotted circles are pixel islands not connected to the brain area that were overpredicted. (c, f) Predictions after postprocessing (in red). The borders of the predicted areas show good agreement with the manual segmentation in both sets. Note that in overlapping manually and automatically segmented areas in b, c, e, and f, colors appear green or purple.

Figure 8. Knowledge input dimension of microlearning-Micro Learning: A Modernized Education System

Figure 8 portrays the research directing the studies on the knowledge input dimension of microlearning. Almost 81% of the respondents believed that microlearning is the best learning system for integrated mashup PLE, followed by 66% of the respondents whose opinion was that dynamic applications of microlearning enhances knowledge. Almost 72% of the respondents believed that microlearning is suitable for diverse subjects, whereas 67% of the respondents believed that the use of digital artifacts maximizes the process of aggregation.

Figure 6. Activities dealing with microlearning-Micro Learning: A Modernized Education System

There are many unique activities involved in microlearning, which provides a high rate of successful knowledge transformation. To the inquiry made in research on the activities dealing with microlearning that support knowledge transformation, figure 6 shows that mind mapping has the highest view of respondents, up to 72%, the nearest to it is story telling with 50%. The variation displayed in the figure is significant. 44% of the respondents opt for tagging followed by text with 38%.

Figure 5. Microlearning dimensions-Micro Learning: A Modernized Education System

The inquiry on the different aspects of the various dimension of microlearning is reflected in figure 5. The result is obtained based on 82 respondents who were learners from different groups. 74% of the respondents consider that process to be a micro learning dimension which strengthens the knowledge input followed by time, up to 60%. The curriculum and mediality had the same 46% of the respondents. 32% and 30% of the respondents believe that the learning type and the content strengthen the knowledge input through microlearning. The lowest percentage is of 20% and 14%.

Figure 2. Current status and necessity of micro learning-Micro Learning: A Modernized Education System

The authors made a percentage analysis based on the basic data collected from the respondents. Out of 100 questionnaires distributed for data collection for the research study, only 91 were received. After reviewing the questionnaires, 7 were incomplete; therefore they were removed from the analysis. The following analysis is based on 84 full and complete questionnaires. To the inquiry on the current status of the learning methodology, 90% of the respondents welcomed a new and smart version of software which in turn leads to micro learning system. 80% of the respondents are currently relying on the work infrastructure to support their learning, whereas 75% of the respondents use more often PLE. 80% of the respondents are looking towards a simple and smart learning system in regular basics and microcontent to transform knowledge.

Figure 4.The purpose for using Smartphones-Micro Learning: A Modernized Education System

Figure 4 shows the purpose of using smartphones. The current status implies that a maximum number of 91% of the respondents use it for social networking. 77% of the respondents use it for communication (WhatsApp, Viber, etc.) followed by e-mails at 55%. Only 43% of the respondents use it for learning and updating (translation, dictionary, etc). Comparing figure 3 andfigure 4 the research identified the gap between electronic device which they prefer for learning and the usage of that electronic devices for learning and updating.

Figure 7. Comparison for identifying the success factor key versus the learning approach-Micro Learning: A Modernized Education System

Figure 1. Mind mapping of Concepts and Versions of Micro learning (Hug, 2005)-Micro Learning: A Modernized Education System

The methods of micro learning are in line with the way that the learner’s brain naturally takes in information, so that the body does not get stressed-out. One of the salient features of micro learning is that it allows the user to find exactly what he or she is looking for. When the mind focuses on a particular question, it is the most open to receiving that answer (www.digitalpromise.org/microcredentials dated on 10/10/2015). It allows the learner’s brain to explore its own curiosity and its own patterns.

Figure 3. Impact of the Boom in digital data in learning-Micro Learning: A Modernized Education System-

Figure 3 portrays the research guiding the impact of the boom in digital data in knowledge codification. The current status shows that 80% of the respondents are interested in learning via electronic devices, followed by e-mails at 75%. 72% and 70% of respondents opt for video clips and sound and voice recording. 65% of the respondents selected images followed by graphical display at 61%. 50% of the respondents selected Journals. Further observation from the figure reveals that books and reference volumes had a very insignificant impact, as expected by merely 45% and 30% of the respondents. This directed us towards the necessity for micro learning, and encourages the increase of the usage of electronic devices.