US 12,001,940 B2
Identifying microorganisms using three-dimensional quantitative phase imaging
Kihyun Hong, Daejeon (KR); Hyun-Seok Min, Daejeon (KR); YongKeun Park, Daejeon (KR); Geon Kim, Daejeon (KR); and Youngju Jo, Daejeon (KR)
Assigned to Tomocube, Inc., Daejeon (KR)
Filed by Tomocube, Inc., Daejeon (KR)
Filed on Sep. 23, 2022, as Appl. No. 17/951,872.
Application 17/951,872 is a continuation of application No. 17/431,871, previously published as PCT/IB2019/058248, filed on Sep. 27, 2019.
Claims priority of provisional application 62/856,290, filed on Jun. 3, 2019.
Claims priority of provisional application 62/817,680, filed on Mar. 13, 2019.
Prior Publication US 2023/0013209 A1, Jan. 19, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/045 (2023.01); G06T 7/00 (2017.01)
CPC G06N 3/045 (2023.01) [G06T 7/0012 (2013.01); G06T 2207/10056 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
using a phase-contrast microscope to generate one or more three-dimensional (3D) quantitative phase images of one or more microscopic entities,
wherein each of the 3D quantitative phase images comprises a respective 3D representation of the one or more microscopic entities as a 3D refractive index tomogram represented as a 3D matrix of numerical values, where each component of the 3D matrix corresponds to a respective 3D spatial position and defines a refractive index at the corresponding 3D spatial position; and
processing the one or more 3D quantitative phase images using a neural network, wherein:
the neural network is a convolutional neural network comprising one or more 3D convolutional layers, wherein each of the 3D convolutional layers performs operations comprising:
receiving a layer input that comprises one or more 3D matrices of features derived from the one or more 3D quantitative phase images input to the neural network; and
processing the layer input to generate a layer output, comprising convolving one or more 3D convolutional filters with the one or more 3D matrices included in the layer input; and
the neural network is configured to process the one or more three-dimensional quantitative phase images in accordance with trained parameter values of the neural network to generate a neural network output characterizing the one or more microscopic entities; and
classifying the one or more microscopic entities using the neural network output.