IP Library › Granted Patent US 12,694,521
Granted Patent B2
US 12,694,521 · App. 18/437,355 · Granted Jul 28, 2026

Apparatus and method for interlocking lesion locations between a guide image and a 3D tomosynthesis images composed of a plurality of 3D image slices

Inventors: Jung Hee Jang (Suwon-si, KR); Do Hyun Lee (Seoul, KR); Woo Suk Lee (Yongin-si, KR); Rae Yeong Lee (Suwon-si, KR)
Assignee: Lunit Inc.
G06T7/0012G06T3/40G06T15/00G06T2207/10072G06T2207/30096G06T2210/41
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Quick Facts
Patent No.
US 12,694,521
App. No.
18/437,355
Filed
Feb 9, 2024
Granted
Jul 28, 2026
Kind
B2
Art Unit
2665
USPC
382/128
Abstract

Provided are a method and an apparatus for interlocking a lesion location between a 2D medical image and 3D tomosynthesis images including a plurality of 3D image slices.

Claims (37)

1 . An apparatus for displaying a guide image and 3D tomosynthesis images comprising a plurality of 3D image slices, the apparatus comprising:

a memory in which at least one program is stored; and

at least one processor operated by executing the at least one program, wherein the at least one processor is configured to:

receive the guide image and the plurality of 3D image slices constituting the 3D tomosynthesis images of a body part;

simultaneously display the guide image showing a plurality of pieces of lesion information detected from the plurality of 3D image slices, and a first 3D image slice included in the plurality of 3D image slices which shows a part of the plurality of pieces of lesion information; and

in response to receiving a user input of selecting a piece of lesion information among the plurality of pieces of lesion information on the guide image:

identify, from among 3D image slices of the plurality of 3D image slices corresponding to the piece of lesion information, a second 3D image slice having a highest lesion score for a particular lesion corresponding to the piece of lesion information, the highest lesion score being indicative of a malignancy of the particular lesion; and

change the first 3D image slice to the second 3D image slice,

wherein the plurality of pieces of lesion information including a location of at least one lesion and a lesion score of the at least one lesion are predicted by using an artificial intelligence model from the plurality of 3D image slices, and

wherein the at least one processor is further configured to:

display, for each of the plurality of pieces of lesion information displayed on the guide image, identification information of the second 3D image slice and the highest lesion score of the second 3D image slice at a position corresponding to an indicator of the second 3D image slice.

2 . The apparatus according to claim 1 , wherein the guide image is one of a 2D medical image, Synthetic 2D or a 3D image slice.

3 . The apparatus according to claim 1 , wherein the at least one processor is configured to enlarge a partial region of the at least the guide image or the second 3D image slice.

4 . The apparatus according to claim 3 , wherein the at least one processor is further configured to:

perform navigation for the partial region of the second 3D image slice in a depth-wise direction based on a user input, and

maintain the 3D image slice in regions other than the enlarged region based on the enlarged region being changed.

5 . The apparatus according to claim 3 , wherein the at least one processor is configured to enlarge the partial region of the second 3D image slice corresponding to the partial region in response to a user input of enlarging the partial region of the guide image.

6 . The apparatus according to claim 1 ,

wherein first coordinate information of a first region in which the piece of lesion information is displayed on the first 3D image slice corresponds to second coordinate information of a second region in which the piece of lesion information is displayed on the guide image.

7 . The apparatus according to claim 1 , wherein the at least one processor is configured to:

display a slice navigation tool in a display region of the second 3D image slice; and

perform navigation for the 3D tomosynthesis images in a depth-wise direction based on a user input received on the slice navigation tool.

8 . The apparatus according to claim 1 , wherein the at least one processor is configured to:

display a pre-stored medical image in response to selection of the piece of lesion information on the second 3D image slice; and

the pre-stored medical image is obtained by an external medical imaging device.

9 . The apparatus according to claim 8 , wherein the at least one processor is configured to process the pre-stored medical image to correspond to depth information of a currently displayed second 3D image slice and position information of a lesion displayed on the second 3D image slice.

10 . The apparatus according to claim 9 , wherein the at least one processor is configured to display a region corresponding to the depth information of the second 3D image slice and the position information of the lesion from among regions of the pre-stored medical image.

11 . A method of displaying a lesion location between a guide image and 3D tomosynthesis images comprising a plurality of three-dimensional (3D) image slices, the method comprising:

receiving the guide image and the plurality of 3D image slices constituting 3D tomosynthesis images of a body part;

simultaneously displaying the guide image showing a plurality of pieces of lesion information detected from the plurality of 3D image slices, and a first 3D image slice included in the plurality of 3D image slices which shows a part of the plurality of pieces of lesion information; and

in response to receiving a user input of selecting a piece of lesion information among the plurality of pieces of lesion information on the guide image:

identifying, from among 3D image slices of the plurality of 3D image slices corresponding to the piece of lesion information, a second 3D image slice having a highest lesion score for a particular lesion corresponding to the piece of lesion information, the highest lesion score being indicative of a malignancy of the particular lesion; and

changing the first 3D image slice to the second 3D image slice,

wherein the plurality of pieces of lesion information including a location of at least one lesion and a lesion score of the at least one lesion are predicted by using an artificial intelligence model from the plurality of 3D image slices, and

wherein the method further comprises:

displaying, for each of the plurality of pieces of lesion information displayed on the guide image, identification information of the second 3D image slice and the highest lesion score of the second 3D image slice at a position corresponding to an indicator of the second 3D image slice.

12 . A non-transitory computer-readable recording medium having recorded thereon a program for executing the method of claim 11 on a computer.

Priority Claims (2)
KR 10-2022-0009411 · Jan 21, 2022 · national
KR 10-2022-0105464 · Aug 23, 2022 · national
Continuity (2)
Continuation 18093162 · Jan 4, 2023
Related Publication 20240249407A1 · Jul 25, 2024
References Cited (31)
US 8634622B2 · Woods et al. · 2014 [cited by applicant]
US 8687867B1 · Collins et al. · 2014 [cited by applicant]
US 8983156B2 · Periaswamy et al. · 2015 [cited by applicant]
US 10916010B2 · Sugahara · 2021 [cited by applicant]
US 11205265B2 · Hall et al. · 2021 [cited by applicant]
US 11935236B2 · Jang · 2024 [cited by examiner]
US 20030007598A1 · Wang · 2003 [cited by examiner]
US 20100135558A1 · Ruth et al. · 2010 [cited by applicant]
US 20150052471A1 · Chen et al. · 2015 [cited by applicant]
US 20150182181A1 · Ruth · 2015 [cited by examiner]
US 20170337336A1 · Weidner · 2017 [cited by examiner]
US 20180158228A1 · Karssemeijer et al. · 2018 [cited by applicant]
US 20200054300A1 · Kreeger et al. · 2020 [cited by applicant]
US 20200253573A1 · Gkanatsios · 2020 [cited by examiner]
US 20200345320A1 · Chen · 2020 [cited by examiner]
US 20210125334A1 · Lotter · 2021 [cited by examiner]
US 20230091506A1 · Lotter · 2023 [cited by examiner]
CN 111340756A · 2020 [cited by examiner]
JP 2009207545A · 2009 [cited by applicant]
JP 2014195729A · 2014 [cited by applicant]
JP 5702041B2 · 2015 [cited by applicant]
JP 6429958B2 · 2018 [cited by applicant]
KR 101923962B1 · 2018 [cited by applicant]
KR 101943011B1 · 2019 [cited by examiner]
Hologic. (Oct. 2019). 3DQuorum™ Imaging Technology [White Paper]. Hologic: Breakthrough Diagnostic & Medical Imaging Solutions. (Year: 2019). [cited by examiner]
Hologic. (Dec. 2020). Genius AI™ Detection for Breast Tomosynthesis [White Paper]. Hologic: Breakthrough Diagnostic & Medical Imaging Solutions. https://www.hologic.com/sites/default/files/2020_12/WP-00178_Rev02_GeniusA… [cited by examiner]
Non-Final Office Action issued in parent U.S. Appl. No. 18/093,162 mailed Apr. 13, 2023. [cited by applicant]
Final Office Action issued in parent U.S. Appl. No. 18/093,162 mailed Aug. 7, 2023. [cited by applicant]
Notice of Allowance issued in parent U.S. Appl. No. 18/093,162 mailed Nov. 15, 2023. [cited by applicant]
Hassan et al., “Mammogram breast cancer CAD systems for mass detection and classification: a review”, Multimedia Tools and Applications, vol. 81, 2022, pp. 20043-20075. [cited by applicant]
Extended European Search Report issued Mar. 31, 2025 in Application No. 22922334.2. [cited by applicant]