Lesion tracking in 4d longitudinal imaging studies

An apparatus comprising:

• means for receiving a first input medical image and a second input medical image each depicting an anatomical object of a patient, the first input medical image comprising a point of interest corresponding to a location of the anatomical object;
• means for extracting, from the first input medical image, a first set of embeddings associated with a plurality of scales using a machine learning based extraction network, the plurality of scales comprising a coarse scale, one or more intermediate scales, and a fine scale;
• means for extracting, from the second input medical image, a second set of embeddings associated with the plurality of scales using the machine learning based extraction network;
• means for determining a location of the anatomical object in the second input medical image by comparing embeddings of the first set of embeddings corresponding to the point of interest with embeddings of the second set of embeddings;
• and
• means for outputting the location of the anatomical object in the second input medical image.

A non-transitory computer readable medium storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations comprising:

• receiving a first input medical image and a second input medical image each depicting an anatomical object of a patient, the first input medical image comprising a point of interest corresponding to a location of the anatomical object;
• extracting, from the first input medical image, a first set of embeddings associated with a plurality of scales using a machine learning based extraction network, the plurality of scales comprising a coarse scale, one or more intermediate scales, and a fine scale;
• extracting, from the second input medical image, a second set of embeddings associated with the plurality of scales using the machine learning based extraction network;
• determining a location of the anatomical object in the second input medical image by comparing embeddings of the first set of embeddings corresponding to the point of interest with embeddings of the second set of embeddings;
• and
• outputting the location of the anatomical object in the second input medical image.