EOS adds to revenue in 2015

AuntMinnie.com staff writers

Jan 6, 2016

Orthopedic digital imaging developer EOS Imaging increased total revenue by 9% in 2015, despite a 60% decline in sales in its Asia-Pacific region.

The company posted total revenue of 21.8 million euros ($23.5 million U.S.) for 2015, compared with 20.1 million euros ($21.7 million) in 2014. While the company reported selling the same number of its EOS imaging systems -- 44 units -- in both 2015 and 2014, it saw higher revenues from service and consumables in the most recent year.

Total revenue in the Asia-Pacific region plummeted by 60% to 2.1 million euros ($2.3 million) last year, compared with 5.5 million euros ($5.9 million) in 2014. The company blamed a transition in its commercial organization for its sales decline in the Asia-Pacific region, adding that it expects the change will have a "positive impact on results in Japan in 2016."

North American sales rose by 75% to 10.4 million euros ($11.2 million) in 2015, compared with 6 million euros ($6.5 million U.S.) in 2014. In Europe and the Middle East, EOS Imaging recorded 9.2 million euros ($9.9 million) in 2015, compared with 8.6 million euros ($9.3 million) in 2014.

The company also anticipates that it will receive regulatory approval for the EOS system in China in 2016, providing another growth opportunity in the Asia-Pacific region.

Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.

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(a) Raw example of a dynamic digital radiograph. (b) Contrast equalization using contrast-limited adaptive equalization shows improved anatomical visualization. (c) Training data is generated through manually labeling using SLEAP’s graphical user interface. (d-e) Neural network detection schematic. For each quadrant a series of two networks were used. The first identifies the quadrant center, the second using input of the first, then identifies individual anatomical landmarks within a quadrant. (d) Four neural networks were trained, each with the objective of identifying a chest quadrant centroid. The four separate quadrants are visualized as colored boxes (right upper quadrant, green; right lower quadrant, blue; left upper quadrant, red; left lower quadrant, yellow). (e) Serial centroid then anatomical identification. Two quadrant examples showing confidence maps of trained neural networks identifying quadrant centroids (left) and then anatomical confidence maps (right). Image and caption courtesy of Chest Pulmonary.

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