TECHNICAL FIELD

Example embodiments generally relate to medical technology and, in particular, relate to apparatuses, systems, and methods for computer-aided detection of biologic abnormalities.

BACKGROUND

Breast cancer is currently the most common cancer for women globally. Over 1.7 million women will be diagnosed with breast cancer and over ½ million women have died due to the disease in 2017 alone. Early detection of breast cancer is known to dramatically improve mortality, with mortality rates being decreased between 20 and 40% depending on the age and frequency of screening mammography. Yet despite these gains, there are severe limitations in the accuracy of conventional breast cancer detection related to the modalities currently available and human limitations in identifying cancers on these modalities. Additionally, the overlap of the appearance of normal and abnormal tissue leads to low specificity and high false positives which are costly for to the health care system and stressful for patients.

X-ray and other imaging technologies operate to assist radiologists with diagnosing a multitude of medical conditions including breast cancer by visually identifying lesions and other tissue abnormalities. These imaging technologies typically capture many images of an area of interest for viewing by a radiologist. In this regard, imaging systems can be employed to capture image data from various viewpoints (e.g., two-dimensional cross-section images of the area of interest, often taken at differing orientations and angles). The raw captured image data is often then processed to transform the data into an image for rendering on a monitor to be viewed by the radiologist. Possibly hundreds or even thousands of views of an area of interest may be rendered and the radiologist may be required to move through those rendered images to identify suspect features that may be indicative of, for example, a cancerous tumor. With that many images to view, the radiologist may, for example, inadvertently miss a potential cancerous tumor.

While imaging technologies continue to advance to provide more contrast and less noise within these image renderings, read times are still long and the frequency of false positives is still too high. Because patients are subjected to the uncomfortable procedures, such as surgical biopsies and the like due to false positives, improvements to reduce read times and decrease the rate of false positive diagnoses would be valuable.

BRIEF SUMMARY OF SOME EXAMPLES

According to some example embodiments, a method for characterizing and locating abnormal tissue in an area of interest in a body is provided. The method may comprise receiving first modality examination data of the area of interest captured by a medical imaging device. The first modality examination data may comprise a plurality of first modality examination data segments, and each first modality examination data segment may be associated with a portion of the area of interest or a first modality sensor field of view into the area of interest. The method may also comprise using a data conditioner computing machine to determine, by evaluating each first modality examination data segment, a set of first modality detection candidates based on the first modality examination data and a first modality feature likelihood model. In this regard, each first modality detection candidate may comprise a first modality likelihood score and a first modality relative location. The method may also comprise generating, by a data fusion machine, a modal correspondence anatomic model of the area of interest by registering the plurality of first modality examination data segments, determining, by a data fusion machine, one or more modal abnormal tissue detections by fusing the set of first modality detection candidates using the modal correspondence anatomic model, and providing, by a data fusion machine, a modal diagnosis confidence score and a modal anatomic location based on the one or more modal abnormal tissue detections. From ingestion of the raw modality examination data to producing abnormal tissue detections, the method is completely automated in computing machines with no human intervention.

According to some example embodiments, a system for characterizing and locating abnormal tissue in an area of interest in a body is provided. The system may comprise processing circuitry configured to receive first modality examination data of the area of interest. The first modality examination data may comprise a plurality of first modality examination data segments, and each first modality examination data segment may be associated with a portion of the area of interest or a first modality sensor field of view into the area of interest. The processing circuitry may be further configured to determine, by evaluating each first modality examination data segment, a set of first modality detection candidates based on the first modality examination data and a first modality feature likelihood model. Each first modality detection candidate may comprise a first modality likelihood score and a first modality relative location. The processing circuitry may be further configured to generate a modal correspondence anatomic model of the area of interest by registering the plurality of first modality examination data segments, determine one or more modal abnormal tissue detections by fusing the set of first modality detection candidates using the modal correspondence anatomic model, and provide a modal diagnosis confidence score and a modal anatomic location based on the one or more modal abnormal tissue detections.

