How automation, machine learning, and DNA barcoding can accelerate species discovery in “dark taxa”: Robotics and AI

Robotics and artificial intelligence are two methods that are suitable for improving processes that are normally done manually. Therefore, these techniques also can be used when examining specimen-rich invertebrate samples, where traditional sorting methods are to slow and require expert knowledge. For that reason, we developed the DiversityScanner: a classification, sorting, and measurement robot for invertebrates. The 500 x 500 x 500 mm robot has three linear axes that enable a camera unit and an automated pipette to be moved over a square Petri dish, containing up to 150 specimens. After starting the DiversityScanner the image taken by an overview camera mounted directly above the Petri dish is utilized to calculate the position of the insects. Then the camera unit is moved over one specimen to capture high resolution detailed images. Convolutional neuronal networks (CNNs) are then used to classify the specimen into 14 different insect taxa (mostly families) and the specimen length and volume are estimated. In a final step, the specimen is moved into a microplate using an automated pipette. In this talk we show how the DiversityScanner uses automation and artificial intelligence to take advantage of previously nearly untapped resources in the study of specimen-rich invertebrate samples

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

The scarcity of high-quality annotated data is omnipresent in machine learning. Especially in biomedical segmentation applications, experts need to spend a lot of their time into annotating due to the complexity. Hence, methods to reduce such efforts are desired. Self-Supervised Learning (SSL) is an emerging field that increases performance when unannotated data is present. However, profound studies regarding segmentation tasks and small datasets are still absent. A comprehensive qualitative and quantitative evaluation is conducted, examining SSL's applicability with a focus on biomedical imaging. We consider various metrics and introduce multiple novel application-specific measures. All metrics and state-of-the-art methods are provided in a directly applicable software package. We show that SSL can lead to performance improvements of up to 10%, which is especially notable for methods designed for segmentation tasks. SSL is a sensible approach to data-efficient learning, especially for biomedical applications, where generating annotations requires much effort. Additionally, our extensive evaluation pipeline is vital since there are significant differences between the various approaches. We provide biomedical practitioners with an overview of innovative data-efficient solutions and a novel toolbox for their own application of new approaches. Our pipeline for analyzing SSL methods is provided as a ready-to-use software package

Improving generative adversarial networks for patch-based unpaired image-to-image translation

Deep learning models for image segmentation achieve high-quality results, but need large amounts of training data. Training data is primarily annotated manually, which is time-consuming and often not feasible for large-scale 2D and 3D images. Manual annotation can be reduced using synthetic training data generated by generative adversarial networks that perform unpaired image-to-image translation. As of now, large images need to be processed patch-wise during inference, resulting in local artifacts in border regions after merging the individual patches. To reduce these artifacts, we propose a new method that integrates overlapping patches into the training process. We incorporated our method into CycleGAN and tested it on our new 2D tiling strategy benchmark dataset. The results show that the artifacts are reduced by 85% compared to state-of-the-art weighted tiling. Additionally, we demonstrate transferability to real-world 3D biological image data, receiving a high-quality synthetic dataset. Increasing the quality of synthetic training datasets can reduce manual annotation, increase the quality of model output, and can help develop and evaluate deep learning model

A Lightweight Framework for Semantic Segmentation of Biomedical Images

We introduce a lightweight framework for semantic segmentation that utilizes structured classifiers as an alternative to deep learning methods. Biomedical data is known for being scarce and difficult to label. However, this framework provides a lightweight, easy-to-apply, and fast-to-train approach that can be adapted to changes in image material though efficient retraining. Moreover, the framework is able to adapt to various input sizes making it robust against changes in resolution and is not tied to specialized hardware, which allows efficient application on standard laptops or desktops without GPUs. We benchmark two distinct models, a single structured classifier and an ensemble of structured classifiers, against a U-Net, evaluating overall performance and training speed. The framework is versatile and can be applied to multi-class semantic segmentation. Our study shows that the proposed framework can effectively compete with established deep learning methods on diverse datasets in terms of performance while reducing training time immensely

Mask R-CNN Outperforms U-Net in Instance Segmentation for Overlapping Cells

U-Net is the go-to approach for biomedical segmentation applications. However, it is not designed to segment overlapping objects, a challenge Mask R-CNN has shown to have great potential in. Yet, Mask R-CNN receives little attention in biomedicine. Hence, we evaluate both approaches on a publicly available biomedical dataset. We find that Mask RCNN outperforms U-Net in segmenting overlapping cells and achieves comparable performance if they do not intersect. Our study provides valuable decision support to practitioners in selecting an appropriate method when solving instance segmentation tasks using deep learning, as well as important insights into enhancing the accuracy of such approaches in biomedical image analysis