# US 12,762,822 B2 · Systems and methods for electronically removing lesions from three-dimensional medical images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,822 B2 - Document ID: US12762822B2 - Status: Granted - Kind: B2 - Application number: 18/161,160 - Filed: 2023-01-30 - Published: 2023-08-03 - Granted: 2026-10-13 - Earliest claimed: 2022-02-02 - Assignees: The University of Chicago - Applicants: The University of Chicago - Inventors: Maryellen Giger; Lindsay Douglas; Deepa Sheth - Web page: https://patentgrep.com/patent/US12762822B2 ## CPC - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V 10/82: using neural networks - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A method for electronically removing a lesion from a three-dimensional (3D) medical image includes segmenting each two-dimensional (2D) slice of a sequence of 2D slices of the 3D medical image to identify the lesion within any one or more of the 2D slices. The method includes deleting the lesion from each 2D slice in which the lesion was identified to create a sequence of lesion-deleted slices. The method includes constructing, based on the sequence of lesion-deleted slices, a lesion-deleted intensity-based projection image, such as a lesion-deleted maximum-intensity projection image. The method includes processing the lesion-deleted intensity-based projection image to obtain a background parenchymal enhancement (BPE) score. The method excludes high-intensity voxels that indicate the presence of a lesion, and thus are not indicative of BPE. ## Claims ### Claim 1 A method for electronically removing a lesion from a three-dimensional (3D) medical image, comprising: segmenting each two-dimensional (2D) slice of a sequence of 2D slices of the 3D medical image to identify the lesion within one or more of the 2D slices; deleting the lesion from each 2D slice in which the lesion was identified to create a sequence of lesion-deleted slices; constructing, based on the sequence of lesion-deleted slices, a lesion-deleted intensity-based projection image; and processing the lesion-deleted intensity-based projection image to obtain a background parenchymal enhancement (BPE) score. ### Claim 2 (depends on 1) The method of claim 1, wherein constructing comprises constructing a lesion-deleted maximum-intensity projection image. ### Claim 3 (depends on 1) The method of claim 1, wherein each 2D slice comprises a dynamic contrast enhanced magnetic resonance image. ### Claim 4 (depends on 1) The method of claim 1, wherein constructing comprises blocking, with a mask, one or more voxels of one or more of the 2D slices. ### Claim 5 (depends on 4) The method of claim 4, further comprising generating a mask for each 2D slice. ### Claim 6 (depends on 5) The method of claim 5, wherein generating the mask comprises: inputting each 2D slice to a trained convolutional neural network (CNN) to obtain a corresponding region-of-interest; and binarizing the region-of-interest to obtain the mask. ### Claim 7 (depends on 6) The method of claim 6, wherein the trained CNN identifies a class label for each voxel of the 2D slice. ### Claim 8 (depends on 1) The method of claim 1, wherein deleting comprises replacing, for each voxel of a plurality of voxels forming the lesion, a value of the voxel with a replacement value. ### Claim 9 A method for electronically removing a lesion from a three-dimensional (3D) medical image, comprising: constructing, based on a sequence of two-dimensional (2D) slices of the 3D medical image, an intensity-based projection image containing a projection of the lesion; segmenting the intensity-based projection image to identify the projection of the lesion; deleting the projection of the lesion from the intensity-based projection image; and processing the intensity-based projection image, after the projection of the lesion is deleted from the intensity-based projection image, to obtain a background parenchymal enhancement (BPE) score. ### Claim 10 A system for electronically removing a lesion from a three-dimensional (3D) medical image, comprising: a processor; a memory communicably coupled with the processor; a lesion deleter implemented as machine-readable instructions that are stored in the memory and, when executed by the processor, control the system to: segment each two-dimensional (2D) slice of a sequence of 2D slices of the 3D medical image to identify the lesion within one or more of the 2D slices, delete the lesion from each 2D slice in which the lesion was identified to create a sequence of lesion-deleted slices, and construct, based on the sequence of lesion-deleted slices, a lesion-deleted intensity-based projection image; and a background parenchymal enhancement (BPE) scorer implemented as machine-readable instructions that are stored in the memory and, when executed by the processor, control the system to process the lesion-deleted intensity-based projection image to obtain a BPE score. ### Claim 11 (depends on 10) The system of claim 10, wherein the machine-readable instructions that, when executed by the processor, control the system to construct include machine-readable instructions that, when executed by the processor, control the system to construct a lesion-deleted maximum-intensity projection image. ### Claim 12 (depends on 10) The system of claim 10, wherein each 2D slice is a dynamic contrast enhanced magnetic resonance image. ### Claim 13 (depends on 10) The system of claim 10, further comprising a voxel blocker implemented as machine-readable instructions that are stored in the memory and, when executed by the processor, control the system to block, with a mask, one or more voxels of one or more of the 2D slices. ### Claim 14 (depends on 13) The system of claim 13, further comprising a mask generator implemented as machine-readable instructions that are stored in the memory and, when executed by the processor, control the system to generate a mask for each 2D slice. ### Claim 15 (depends on 14) The system of claim 14, the mask generator including additional machine-readable instructions that, when executed by the processor, control the system to: input the 2D slice to a trained convolutional neural network (CNN) to obtain a corresponding region-of-interest, and binarize the region-of-interest to obtain the mask. ### Claim 16 (depends on 15) The system of claim 15, wherein the trained CNN identifies a class label for each voxel of the 2D slice. ### Claim 17 (depends on 10) The system of claim 10, wherein the machine-readable instructions that, when executed by the processor, control the system to segment the 2D slice include machine-readable instructions that, when executed by the processor, control the system to cluster the 2D slice. ### Claim 18 (depends on 10) The system of claim 10, further comprising a medical imaging device for capturing the 3D medical image.