EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control

Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Maier, Jennifer; Bergler, Christian; Kröger, Christine; Voigt, Jörn; Breininger, Katharina; Klopfleisch, Robert; Maier, Andreas

doi:10.1038/s41598-021-83827-4

Cited by 35 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Annotation data collection was acquired in a two-step process performed by three trained biologists. In the first step, they labeled each image in a competitive process (using the EXACT labeling tool [31]), where each expert created bounding box annotations for each animal, including a label for their identities, not knowing about the annotations made by the other experts. The global accordance metric after the first labeling round was R = 0.938.…”

Section: Labeling Processmentioning

confidence: 99%

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears

Zuerl

Stoll

Brehm

et al. 2022

Animals

View full text Add to dashboard Cite

The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals’ physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (Ursus maritimus). We show that the framework can localize and identify individual polar bears with an F1 score of 86.4%. The localization accuracy of the framework is 19.9±7.6 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo.

show abstract

Section: Labeling Processmentioning

confidence: 99%

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears

Zuerl

Stoll

Brehm

et al. 2022

Animals

View full text Add to dashboard Cite

show abstract

“…The four test patches for evaluation were manually annotated into HER2 scored tumor areas, non-tumor areas and staining artefacts. These areas were segmented using polygons in the EXACT tool [10]. Annotations were performed by a medical student and reviewed by a board-certified pathologist.…”

Section: Datasetmentioning

confidence: 99%

Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation

Öttl¹,

Mönius²,

Marzahl³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Supervised deep learning has shown state-of-the-art performance for medical image segmentation across different applications, including histopathology and cancer research; however, the manual annotation of such data is extremely laborious. In this work, we explore the use of superpixel approaches to compute a pre-segmentation of HER2 stained images for breast cancer diagnosis that facilitates faster manual annotation and correction in a second step. Four methods are compared: Standard Simple Linear Iterative Clustering (SLIC) as a baseline, a domain adapted SLIC, and superpixels based on feature embeddings of a pretrained ResNet-50 and a denoising autoencoder. To tackle oversegmentation, we propose to hierarchically merge superpixels, based on their content in the respective feature space. When evaluating the approaches on fully manually annotated images, we observe that the autoencoder-based superpixels achieve a 23% increase in boundary F1 score compared to the baseline SLIC superpixels. Furthermore, the boundary F1 score increases by 73% when hierarchical clustering is applied on the adapted SLIC and the autoencoder-based superpixels. These evaluations show encouraging first results for a pre-segmentation for efficient manual refinement without the need for an initial set of annotated training data.

show abstract

“…Overall, annotations were gathered for seven canine cutaneous tumor subtypes as well as six additional tissue classes: epidermis, dermis, subcutis, bone, cartilage and a joint class of inflammation and necrosis. For database review, the open source online platform EXACT 27 was used to monitor slide and annotation completeness.…”

Section: Annotation Workflowmentioning

confidence: 99%

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Wilm¹,

Fragoso²,

Marzahl³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset consisting of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes including seven cutaneous tumor subtypes. Regarding sample size and annotation extent, this exceeds most publicly available datasets which are oftentimes limited to the tumor area or merely provide patch-level annotations. We validated our model for tissue segmentation, achieving a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For tumor subtype classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors, we believe that the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.

show abstract

EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control

Cited by 35 publications

References 21 publications

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears

Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Contact Info

Product

Resources

About