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Automated segmentation of insect anatomy from micro‐CT images using deep learning
No full textToulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel, Doya, Kenji (2023): Automated segmentation of insect anatomy from micro-CT images using deep learning. Natural Sciences (e20230010) 3 (4): 1-12, DOI: 10.1002/ntls.2023001
FIGURE 3 in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 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.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 6, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
FIGURE 6 in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 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.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 8, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
FIGURE 8 3D in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 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.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 9, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
FIGURE 4 in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 4 Data augmentation. (a) Initial 2D image of a full head scan of an Atta texana ant specimen (original 1000 × 1000 px). Preprocessing is performed in two steps: (b) The image is cropped (original 520 × 520 px) around the brain area, keeping some of the muscles, nerves, and fibers that are close (or even attached) to the brain. The manual segmentation of the brain is indicated in blue. (c) Histogram equalization is used for additional augmentation, which enhances the contrast and projects the inner parts of the brain more clearly.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 6, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
FIGURE 9 in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 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.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 10, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
FIGURE 5 U in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 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.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 7, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
FIGURE 2 in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 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.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 5, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
FIGURE 10 in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 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.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 10, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
FIGURE 7 in Automated segmentation of insect anatomy from micro-CT images using deep learning
No full textFIGURE 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.Published as part of <i>Toulkeridou, Evropi, Economo, Evan P., Gutierrez, Carlos Enrique, Baum, Daniel & Doya, Kenji, 2023, Automated segmentation of insect anatomy from micro-CT images using deep learning, pp. 1-12 in Natural Sciences (e20230010) 3 (4)</i> on page 9, DOI: 10.1002/ntls.20230010, <a href="http://zenodo.org/record/10076519">http://zenodo.org/record/10076519</a>
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