Methods and systems for image selection

A method comprising:

• receiving a localizer image of an imaging subject and a plurality of image slices corresponding to the localizer image;
• extracting a plurality of features from the localizer image;
• mapping the plurality of features to one or more bounding boxes for one or more pre-determined regions of the imaging subject using a trained machine learning model;
• resolving boundaries of the one or more bounding boxes to produce a plurality of non-intersecting regions;
• and
• responding to a region of the plurality of non-intersecting regions being a target region by:
• correlating coordinates of the region from a coordinate system of the localizer image to a coordinate system of the plurality of image slices to produce a target image slice coordinate range;
• and
• labeling a subset of the plurality of image slices as belonging to the target region of the imaging subject based on the target image slice coordinate range,
• wherein correlating coordinates of the region from the coordinate system of the localizer image to the coordinate system of the plurality of image slices to produce the target image slice coordinate range, comprises, mapping a first boundary and a second boundary of the region from a first axis in the coordinate system of the localizer image, to a second axis in the coordinate system of the plurality of image slices, to produce the target image slice coordinate range, wherein the plurality of image slices are indexed based on distance along the second axis;
• wherein distance along the first axis is measured in pixels, and distance along the second axis is measured in millimeters, and wherein a relationship between distance along the first axis and distance along the second axis is given by:
• Z
• k
• =
• Z
• ref
• +
• Y
• k
• *
• Spacing
• Z
• Y
• *
• cos
• ⁢
• θ
• where Zk is a distance of a point k along the second axis, Zref is an offset between an origin of the first axis and an origin of the second axis, Yk is a distance of the point k along the first axis, Spacing is a ratio between distance along the second axis and distance along the first axis, and θ is an angle between the first axis and the second axis, wherein when θ equals 0, the first axis is parallel to the second axis.

A method, comprising:

• receiving a localizer image;
• pre-processing the localizer image to form a pre-processed image based on an architecture of a trained machine learning model;
• mapping the pre-processed image to one or more bounding boxes;
• resolving boundaries of the one or more bounding boxes to produce a plurality of non-intersecting regions by extracting boundary box borders and determining if the boundary box borders overlap;
• and
• classifying a plurality of axial images into one or more classes based on the plurality of non-intersecting regions.

A system comprising:

• a non-transitory memory, wherein the non-transitory memory stores instructions, and a trained machine learning model;
• a display device;
• and
• a processor, wherein the processor is communicably coupled to the non-transitory memory and the display device, and wherein the processor, when executing the instructions, is configured to:
• receive a localizer image of an imaging subject and a plurality of image slices corresponding to the localizer image;
• map the localizer image to one or more bounding boxes using the trained machine learning model;
• post-process the one or more bounding boxes to produce a coherent set of bounding boxes;
• resolve boundaries of the coherent set of bounding boxes to produce a plurality of non-intersecting regions by extracting boundary box borders and determining if the boundary box borders overlap;
• correlate coordinates of a region of the plurality of non-intersecting regions from a coordinate system of the localizer image to a coordinate system of the plurality of image slices to produce an image slice coordinate range;
• label a subset of the plurality of image slices as belonging to the region of the imaging subject based on the image slice coordinate range;
• and
• displaying the subset of the plurality of image slices via the display device.

A system comprising:

• a non-transitory memory, wherein the non-transitory memory stores instructions, and a trained machine learning model;
• a display device;
• and
• a processor, wherein the processor is communicably coupled to the non-transitory memory and the display device, and wherein the processor, when executing the instructions, is configured to:
• receive a localizer image of an imaging subject and a plurality of image slices corresponding to the localizer image;
• map the localizer image to one or more bounding boxes using the trained machine learning model;
• post-process the one or more bounding boxes to produce a coherent set of bounding boxes;
• resolve boundaries of the coherent set of bounding boxes to produce a plurality of non-intersecting regions;
• correlate coordinates of a region of the plurality of non-intersecting regions from a coordinate system of the localizer image to a coordinate system of the plurality of image slices to produce an image slice coordinate range;
• label a subset of the plurality of image slices as belonging to the region of the imaging subject based on the image slice coordinate range;
• and
• displaying the subset of the plurality of image slices via the display device;
• wherein the processor is configured to correlate coordinates of the region of the plurality of non-intersecting regions from the coordinate system of the localizer image to the coordinate system of the plurality of image slices to produce the image slice coordinate range by:
• mapping a first boundary and a second boundary of the region from a first axis in the coordinate system of the localizer image, to a second axis in the coordinate system of the plurality of image slices, to produce the image slice coordinate range,
• wherein a mapping between the first axis and the second axis is given by:
• Z
• k
• =
• Z
• ref
• +
• Y
• k
• *
• Spacing
• Z
• Y
• *
• cos
• ⁢
• θ
• where Zk is a distance of a point k along the second axis, Zref is an offset between an origin of the first axis and an origin of the second axis, Yk is a distance of the point k along the first axis, Spacing is a ratio between distance along the second axis and distance along the first axis, and θ is an angle between the first axis and the second axis.