Granted Patent
B2
US 12,229,949 · App. 17/401,536 · Granted Feb 18, 2025
Systems for automated lesion detection and related methods
Inventors:
Engin Dikici (Dublin, OH); Luciano Prevedello (Dublin, OH); Matthew Bigelow (Hilliard, OH)
Assignee:
Ohio State Innovation Foundation
G06T7/0012G06F18/214G06F18/217G06F18/243G06N3/04G06N3/045G06N3/088G06T11/60G06V10/255G06T2207/10081G06T2207/10088G06T2207/10104G06T2207/10132G06T2207/30096
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Abstract
Example systems and methods for lesion detection are described herein. An example system includes at least one processor and a memory operably coupled to the at least one processor. The system also includes a candidate selection module configured to receive an image, determine a plurality of candidate points in the image, and select a respective volumetric region centered by each of the candidate points. A portion of a lesion has a high probability of being determined as a candidate point. The system further includes a deep learning network configured to receive the respective volumetric regions selected by the candidate selection module, and determine a respective probability of each respective volumetric region to contain the lesion. Additionally, example methods for training a deep learning network to detect lesions are described herein.
Claims (29)
1. A system for lesion detection, comprising:
at least one processor;
a memory operably coupled to the at least one processor, the memory having computer-executable instructions stored thereon;
a candidate selection module stored in the memory that, when executed by the at least one processor, is configured to:
receive an image,
determine a plurality of candidate points in the image using a Laplacian of Gaussian (LoG) approach with sensitivity constraint, wherein the sensitivity constraint is selected such that a desired percentage of actual lesions having a volume less than 500 mm 3 are detected as candidate points, and
select a respective volumetric region centered by each of the candidate points, wherein one or more of the respective volumetric regions contain lesions having a volume of less than 500 mm 3 ; and
a deep learning network configured to:
receive the respective volumetric regions selected by the candidate selection module, and
determine a respective probability of each respective volumetric region to contain a lesion.
2. The system of claim 1 , wherein the deep learning network is further configured to classify each respective volumetric region as a positive or negative lesion candidate based on its respective probability to contain the lesion.
3. The system of claim 2 , further comprising an image annotation module stored in the memory that, when executed by the at least one processor, is configured to provide an annotation to highlight within the image a volumetric region classified as the positive lesion candidate.
4. The system of claim 3 , further comprising a display device, wherein the display device is configured to display the image and the annotation.
5. The system of claim 1 , wherein the deep learning network is a convolutional neural network (CNN).
6. The system of claim 1 , wherein the volumetric region is a 16 millimeter (mm)×16 mm×16 mm region, a 32 mm×32 mm×32 mm region, or a 64 mm×64 mm×64 mm region.
7. The system of claim 1 , wherein the image is a magnetic resonance imaging (MRI) image, a computed tomography (CT) image, a positron emission tomography (PET)-CT image, a three-dimensional (3D) mammography image, or a 3D ultrasound image.
8. The system of claim 1 , wherein the lesion is a brain metastatic (BM) lesion.
9. A computer-implemented method for lesion detection, comprising:
receiving an image;
determining a plurality of candidate points in the image using a Laplacian of Gaussian (LoG) approach with sensitivity constraint, wherein the sensitivity constraint is selected such that a desired percentage of actual lesions having a volume less than about 500 mm 3 are detected as candidate points;
selecting a respective volumetric region centered by each of the candidate points, wherein one or more of the respective volumetric regions contain lesions having a volume of less than 500 mm 3 ;
inputting each respective volumetric region into a deep learning network; and
determining, using the deep learning network, a respective probability of each respective volumetric region to contain a lesion.
10. The computer-implemented method of claim 9 , wherein the deep learning network is a convolutional neural network (CNN).
11. The computer-implemented method of claim 9 , further comprising:
classifying each respective volumetric region as a positive or negative lesion candidate based on its respective probability to contain the lesion;
displaying the image; and
providing an annotation within the image to highlight a volumetric region classified as the positive lesion candidate.
12. The computer-implemented method of claim 9 , wherein the image is a magnetic resonance imaging (MRI) image, a computed tomography (CT) image, a positron emission tomography (PET)-CT image, a three-dimensional (3D) mammography image, or a 3D ultrasound image.
Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST
Recorded Nov 16, 2021
From: DIKICI, ENGIN; PREVEDELLO, LUCIANO; BIGELOW, MATTHEW
To: OHIO STATE INNOVATION FOUNDATION
Reel/Frame 058120/0564 →
Continuity (2)
Provisional Application
63065015
· Aug 13, 2020
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