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1. Measuring mantled howler monkey ( Alouatta palliata ) testes via parallel laser photogrammetry: Expanding the use of noninvasive methods

   https://doi.org/10.1002/ajp.23616  

   Ehrie, Austen J; Iruri‐Tucker, Alec A; Lord, Yasmin B; Williamson, Heidi G; Hunt, Kevin D; Polly, P David; Fitzpatrick, Courtney L; Wasserman, Michael D (June 2024, American Journal of Primatology)

   Abstract Parallel laser photogrammetry (PLP), which consists of attaching two or three parallel laser beams at a known inter‐beam distance to a camera, can be used to collect morphological measurements of organisms noninvasively. The lasers project onto the photo being taken, and because the inter‐beam distance is known, they act as a scale for image analysis programs like ImageJ. Traditionally, this method has been used to measure larger morphological traits (e.g., limb length, crown‐rump length) to serve as proxies for overall body size, whereas applications to smaller anatomical features remain limited. To that end, we used PLP to measure the testes of 18 free‐living mantled howler monkeys (Alouatta palliata)at La Selva Biological Station, Costa Rica. We tested whether this method could reliably measure this relatively small and globular morphology, and whether it could detect differences among individuals. We tested reliability in three ways: within‐photo (coefficient of variation [CV] = 4.7%), between‐photo (CV = 5.5%), and interobserver (intraclass correlation = 0.92). We found an average volume of 36.2 cm³and a range of 16.4–54.4 cm³, indicating variation in testes size between individuals. Furthermore, these sizes are consistent with a previous study that collected measurements by hand, suggesting that PLP is a useful method for making noninvasive measurements of testes. 

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2. Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys

   https://doi.org/10.1038/s41598-023-28240-9  

   Lenzi, Javier; Barnas, Andrew F.; ElSaid, Abdelrahman A.; Desell, Travis; Rockwell, Robert F.; Ellis-Felege, Susan N. (December 2023, Scientific Reports)

   Abstract Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou ( Rangifer tarandus ), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: “ adult caribou ”, “ calf caribou ”, and “ ghost caribou ” (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96–0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers’ annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities. 

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3. Optimal Transport and Contrastive-Based Clustering for Annotation-Free Tissue Analysis in Histopathology Images

   https://doi.org/10.1109/ICMLA58977.2023.00049  

   Aburidi, Mohammed; Marcia, Roummel (December 2023, 2023 International Conference on Machine Learning and Applications (ICMLA))

   Training a deep learning model with a large annotated dataset is still a dominant paradigm in automatic whole slide images (WSIs) processing for digital pathology. However, obtaining manual annotations is a labor-intensive task, and an error-prone to inter and intra-observer variability. In this study, we offer an online deep learning-based clustering workflow for annotating and analysis of different types of tissues from histopathology images. Inspired by learning and optimal transport theory, our proposed model consists of two stages. In the first stage, our model learns tissue-specific discriminative representations by contrasting the features in the latent space at two levels, the instance- and the clusterlevel. This is done by maximizing the similarities of the projections of positive pairs (views of the same image) while minimizing those of negative ones (views of the rest of the images). In the second stage, our framework extends the standard cross-entropy minimization to an optimal transport problem and solves it using the Sinkhorn-Knopp algorithm to produce the cluster assignments. Moreover, our proposed method enforces consistency between the produced assignments obtained from views of the same image. Our framework was evaluated on three common histopathological datasets: NCT-CRC, LC2500, and Kather STAD. Experiments show that our proposed framework can identify different tissues in annotation-free conditions with competitive results. It achieved an accuracy of 0.9364 in human lung patched WSIs and 0.8464 in images of human colorectal tissues outperforming state of the arts contrastive-based methods. 

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4. Automated extraction of right whale morphometric data from drone aerial photographs

   https://doi.org/10.1002/rse2.70001  

   Bagchi, Chhandak; Medina, Josh; Irschick, Duncan J; Maji, Subhransu; Christiansen, Fredrik (August 2025, Remote Sensing in Ecology and Conservation)

   Abstract Aerial photogrammetry is a popular non‐invasive tool to measure the size, body morphometrics and body condition of wild animals. While the method can generate large datasets quickly, the lack of efficient processing tools can create bottlenecks that delay management actions. We developed a machine learning algorithm to automatically measure body morphometrics (body length and widths) of southern right whales (Eubalaena australis, SRWs) from aerial photographs (n = 8,958) collected by unmanned aerial vehicles in Australia. Our approach utilizes two Mask R‐CNN detection models to: (i) generate masks for each whale and (ii) estimate points along the whale's axis. We annotated a dataset of 468 images containing 638 whales to train our models. To evaluate the accuracy of our machine learning approach, we compared the model‐generated body morphometrics to manual measurements. The influence of picture quality (whale posture and water clarity) was also assessed. The model‐generated body length estimates were slightly negatively biased (median error of −1.3%), whereas the body volume estimates had a small (median error of 6.5%) positive bias. After correcting both biases, the resulting model‐generated body length and volume estimates had mean absolute errors of 0.85% (SD = 0.75) and 6.88% (SD = 6.57), respectively. The magnitude of the errors decreased as picture quality increased. When using the model‐generated data to quantify intra‐seasonal changes in body condition of SRW females, we obtained a similar slope parameter (−0.001843, SE = 0.000095) as derived from manual measurements (−0.001565, SE = 0.000079). This indicates that our approach was able to accurately capture temporal trends in body condition at a population level. 

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5. Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

   https://doi.org/10.1039/D2DD00066K  

   Lu, Shizhao; Montz, Brian; Emrick, Todd; Jayaraman, Arthi (December 2022, Digital Discovery)

   In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models requires large numbers of images with associated structural labels, however, manual labeling of images requires domain knowledge and is prone to human error and subjectivity. To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets. Specifically, we train an image encoder with unlabeled images using self-supervised learning methods and use that encoder for transfer learning of different downstream image tasks (classification and segmentation) with a minimal number of labeled images for training. We test the transfer learning ability of two self-supervised learning methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM) images. We demonstrate in detail how this machine learning workflow applied to TEM images of protein nanowires enables automated classification of nanowire morphologies ( e.g. , single nanowires, nanowire bundles, phase separated) as well as segmentation tasks that can serve as groundwork for quantification of nanowire domain sizes and shape analysis. We also extend the application of the machine learning workflow to classification of nanoparticle morphologies and identification of different type of viruses from TEM images. 

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