Regional woes hurt IDC's Q2 results

AuntMinnie.com staff writers

May 15, 2012

Revenue shortfalls from the Asia Pacific and Latin America regions adversely affected results for digital radiography vendor Imaging Dynamics (IDC) in the first quarter (end-March 31).

Gross revenues declined 46% to $1.1 million, compared with $2 million in the same quarter of 2011. The net loss for the quarter was down to $309,000, compared with a net loss of $776,000 in the year-ago quarter.

IDC president and CEO Swapan Kakumanu said the results were below expectations, mainly due to the reduction in expected revenues from Asia Pacific and Latin America. He added that the company continued to reduce overhead expenses and manage its working capital.

In addition, IDC is working with the Toronto Stock Exchange (TSX) on the listing of the firm's shares. TSX announced last month that it had begun a review of whether IDC met the exchange's listing requirements. IDC has 60 days from the April 24 start of the review to regain compliance with the requirements.

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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(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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