Transforming the Visualization of Breast Health

Imago’s sophisticated software delivers new levels of visual intelligence, helping to support more confident diagnosis and better patient outcomes.

Left: Original mammogram taken three years before patient was diagnosed with cancer. Right: Imago ICE Contours algorithm reveals patterns of increasing density in location where cancer was diagnosed three years later.

Enabling Earlier Identification of Abnormalities

Imago ICE Reveal is a transformational product, the first to provide a comprehensive view of the breast’s health.

ICE Reveal delivers a suite of new visualizations intended to improve both sensitivity and specificity when interpreting a mammogram. Each visualization has been developed to provide a unique presentation of the mammogram, and is specifically tuned to the human vision system, allowing for rapid identification of abnormalities.

Left: Original mammogram taken three years before patient was diagnosed with cancer. Right: Imago ICE Contours algorithm reveals patterns of increasing density in location where cancer was diagnosed three years later.
Left: Close-up of original mammogram with known cancer present in dense breast tissue. Right: Canyons reveals the patterns associated with the margins and internal structures of the lesion, which in this image contains DCIS and Tubular Carcinoma.

Solving the Dense Breast Challenge

The ICE Reveal product provides clinicians with unique characterizations of each tissue in the breast image even within the highest density areas of the mammogram. The Canyons algorithm image provides both color and texture differentiation between normal and abnormal tissues.

Providing Specific, Accurate, and Objective Clinical Data

Imago Ice

Visually accentuates the characteristics of “patterns” of tissues that bear the highest potential for being abnormal

Reveals the presence, margins (boundaries), and internal structures of lesions in all areas of the breast in a mammogram, even in those that are extremely dense

Highlights the presence of microcalcifications and reveals their associated masses

Clarifies low-attenuating masses, calcifications, and structural details of lymph nodes

Isolates regions of interest as the basis for optimizing its machine learning and artificial intelligence development programs

Shows areas of possible abnormalities within dense breast tissue by greatly increasing visual distinction whenever structures occur within other structures and appear white-on-white or gray-on-gray

Creates highly distinguishable characterizations of breast structures via color patterns

Original mammogram with known cancerous lesion above and benign lesion below.
Left: Imago’s ICE Contours algorithm displays dense concentric patterns within the cancerous tissue, differentiated from a more “open” pattern characterizing the benign tissue. Right: The ICE Color Island algorithm differentially characterizes the cores of the two lesions.
Close-up of high-density original mammogram with known calcifications and malignant mass.
Imago’s ICE Relief algorithm reveals structural characteristics within the mass.
Original mammogram with known multi-focal masses and clusters of microcalcifications.
Top: Close-up of section of original mammogram.

Bottom: Calcifications and associated tissue structures that are further delineated and easier to discern after processing with ICE Microcalcifications algorithm.
Close-up of original mammogram with highly-visible cluster of calcifications and less-distinct malignant mass.
Imago’s ICE Low Density algorithm reveals the internal structures and extent of the margins of the mass associated with the calcifications.
Left and right CC mammogram views of breast with silicone implants and leakage occurring at arrows.
Imago’s ICE Symmetry algorithm characterizes the internal structure of each breast and its implant, the change in silicone distribution due to the leakage, and the “flow disturbance” patterns created by the leakage.
Original mammogram with very dense breast tissue.
Imago’s ICE Relief algorithm characterizes tissue structures associated with the known underlying cancerous lesion and simplifies their presentation.
X-ray of cancerous lesion tissue extracted during surgery, with areas of normal tissue, mass margins, and cancerous core tissue marked accordingly.
Imago’s ICE Color Island algorithm differentiates normal tissue, cancer margins, and the internal “geometry” of the cancerous core in the extracted tissue.