公开(公告)号：US20250061605A1

公开(公告)日：2025-02-20

申请号：US18450196

申请日：2023-08-15

Applicant: GE Precision Healthcare LLC

Inventor： Sandeep Dutta ,  Christopher Philip Bridge ,  Charles Jiali Lu ,  Mitchel B Harris ,  Bharti Khurana ,  Praveer Singh ,  Mehak Aggarwal ,  Sujay Shivanand Kakarmath ,  Ashwin Vaswani ,  Amy Deubig ,  Saad Sirohey ,  Jayashree Kalpathy-Cramer

IPC: G06T7/73 ,  G06T7/00 ,  G16H30/40 ,  G16H50/50

Abstract: Systems or techniques that facilitate hybrid 3D-to-2D slice-wise object localization ensembles are provided. In various embodiments, a system can access at least one three-dimensional voxel array. In various aspects, the system can localize, via execution of a deep learning ensemble, an object depicted in the at least one three-dimensional voxel array. In various instances, the deep learning ensemble can receive as input the at least one three-dimensional voxel array. In various cases, the deep learning ensemble can produce as output a set of two-dimensional object location indicators respectively corresponding to a set of two-dimensional slices of the at least one three-dimensional voxel array.

公开(公告)号：US12217417B2

公开(公告)日：2025-02-04

申请号：US17470076

申请日：2021-09-09

Applicant: GE Precision Healthcare LLC ,  University of Zurich

Inventor： Sidharth Abrol ,  Bipul Das ,  Vanika Singhal ,  Amy Deubig ,  Sandeep Dutta ,  Daphné Gerbaud ,  Bianca Sintini ,  Ronny Büchel ,  Philipp Kaufmann

IPC: G06T7/00 ,  G06N20/00 ,  G06T5/60 ,  G06T5/70 ,  G06T7/10 ,  G06T7/11 ,  G06V10/25 ,  G16H30/40

Abstract: Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

公开(公告)号：US20230071535A1

公开(公告)日：2023-03-09

申请号：US17470076

申请日：2021-09-09

Applicant: GE Precision Healthcare LLC ,  University of Zurich

Inventor： Sidharth Abrol ,  Bipul Das ,  Vanika Singhal ,  Amy Deubig ,  Sandeep Dutta ,  Daphné GERBAUD ,  Bianca Sintini ,  Ronny BÜCHEL ,  Philipp KAUFMANN

IPC: G06T7/00 ,  G06T5/00 ,  G06T7/11 ,  G06N20/00 ,  G16H30/40 ,  G06K9/32

Abstract: Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.