公开(公告)号：US11823106B2

公开(公告)日：2023-11-21

申请号：US16365780

申请日：2019-03-27

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Li Yao ,  Eric C. Poblenz ,  Jordan Prosky ,  Ben Covington ,  Anthony Upton

IPC: G16H30/40 ,  G06N5/04 ,  G16H50/70 ,  G06Q10/06 ,  G06T7/187 ,  G06T7/44 ,  G06T7/10 ,  G06T7/11 ,  G16H40/20 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H50/20 ,  G16H10/20 ,  G06F16/245 ,  G06N20/20 ,  G06N20/00 ,  G06V10/25 ,  G06V10/82 ,  G06V10/764 ,  G06V30/19 ,  H04L67/01 ,  G06F18/2115 ,  G06F18/214 ,  G06F18/2415 ,  G06F3/0482 ,  G06F3/0484 ,  G06N5/045 ,  G06Q20/14 ,  G06T3/40 ,  G06T5/50 ,  G06T7/12 ,  H04L67/12 ,  G06T7/70 ,  G16H50/30 ,  G06F40/295 ,  G06V30/194 ,  G06F18/24 ,  A61B5/055 ,  G06Q50/22 ,  G06Q10/0631 ,  G06T5/00 ,  G06T7/00 ,  G06T11/00 ,  G06F9/54 ,  A61B5/00 ,  G06F21/62 ,  G06T11/20 ,  G06F18/40 ,  G06F18/21 ,  G06V40/16 ,  G06V10/22 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06F18/2111

CPC classification number: G06Q10/06315 ,  A61B5/7264 ,  G06F3/0482 ,  G06F3/0484 ,  G06F9/542 ,  G06F16/245 ,  G06F18/214 ,  G06F18/217 ,  G06F18/2115 ,  G06F18/2415 ,  G06F18/41 ,  G06F21/6254 ,  G06N5/04 ,  G06N5/045 ,  G06N20/00 ,  G06N20/20 ,  G06Q20/14 ,  G06T3/40 ,  G06T5/002 ,  G06T5/008 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/10 ,  G06T7/11 ,  G06T7/187 ,  G06T7/44 ,  G06T7/97 ,  G06T11/001 ,  G06T11/006 ,  G06T11/206 ,  G06V10/225 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/171 ,  G16H10/20 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/20 ,  H04L67/01 ,  H04L67/12 ,  A61B5/055 ,  A61B6/032 ,  A61B6/5217 ,  A61B8/4416 ,  G06F18/2111 ,  G06F18/24 ,  G06F40/295 ,  G06Q50/22 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30008 ,  G06T2207/30016 ,  G06T2207/30061 ,  G06V30/194 ,  G06V2201/03 ,  G16H50/30 ,  G16H50/70

Abstract: A location-based medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans. Location-based subsets of the plurality of medical scans are generated by including ones of the plurality of medical scans with originating locations that compare favorably to location grouping criteria for the each location-based subset. A plurality of location-based models are generated by performing a fine-tuning step on the generic model, utilizing a corresponding one of the plurality of location-based subsets. Inference data is generated for a new medical scan by utilizing one of the location-based models on the new medical scan, where an originating location associated with the new medical scan compares favorably to location grouping criteria for the location-based subset utilized to generate the location-based model. The inference data is transmitted to a client device for display via a display device.

公开(公告)号：US11694137B2

公开(公告)日：2023-07-04

申请号：US17656526

申请日：2022-03-25

Applicant: Enlitic, Inc.

Inventor： Li Yao ,  Jordan Prosky ,  Eric C. Poblenz ,  Kevin Lyman ,  Ben Covington ,  Anthony Upton

IPC: G06Q10/0631 ,  G16H10/60 ,  G16H30/40 ,  G16H15/00 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G16H10/20 ,  G06N5/045 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  H04L67/01 ,  G06V10/82 ,  G06F18/40 ,  G06F18/214 ,  G06F18/21 ,  G06F18/2115 ,  G06F18/2415 ,  G06V10/25 ,  G06V30/19 ,  G06V10/764 ,  G06V40/16 ,  G06V10/22 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06F18/24 ,  G06F18/2111 ,  G06V30/194

CPC classification number: G06Q10/06315 ,  A61B5/7264 ,  G06F3/0482 ,  G06F3/0484 ,  G06F9/542 ,  G06F16/245 ,  G06F18/214 ,  G06F18/217 ,  G06F18/2115 ,  G06F18/2415 ,  G06F18/41 ,  G06F21/6254 ,  G06N5/04 ,  G06N5/045 ,  G06N20/00 ,  G06N20/20 ,  G06Q20/14 ,  G06T3/40 ,  G06T5/002 ,  G06T5/008 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/10 ,  G06T7/11 ,  G06T7/187 ,  G06T7/44 ,  G06T7/97 ,  G06T11/001 ,  G06T11/006 ,  G06T11/206 ,  G06V10/225 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/171 ,  G16H10/20 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/20 ,  H04L67/01 ,  H04L67/12 ,  A61B5/055 ,  A61B6/032 ,  A61B6/5217 ,  A61B8/4416 ,  G06F18/2111 ,  G06F18/24 ,  G06F40/295 ,  G06Q50/22 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30008 ,  G06T2207/30016 ,  G06T2207/30061 ,  G06V30/194 ,  G06V2201/03 ,  G16H50/30 ,  G16H50/70

Abstract: A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

公开(公告)号：US11551795B2

公开(公告)日：2023-01-10

申请号：US17666813

申请日：2022-02-08

Applicant: Enlitic, Inc.

Inventor： Li Yao ,  Jordan Prosky ,  Eric C. Poblenz ,  Kevin Lyman ,  Lionel Lints ,  Ben Covington ,  Anthony Upton

IPC: G06T7/00 ,  G16H10/60 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  G06V10/22 ,  H04L67/01 ,  G06V10/82 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06V30/194

Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.

公开(公告)号：US20210295966A1

公开(公告)日：2021-09-23

申请号：US17336648

申请日：2021-06-02

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Li Yao ,  Eric C. Poblenz ,  Jordan Prosky ,  Ben Covington ,  Anthony Upton

IPC: G16H10/60 ,  H04L29/06 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  G06K9/20 ,  H04L29/08

Abstract: An intensity transform augmentation system is operable to generate a plurality of sets of augmented images by performing a set of intensity transformation functions on each of a training set of medical scans. Each of the set of intensity transformation functions are based on density properties of corresponding anatomy feature present in the training set of medical scans. A computer vision model is generated by performing a training step on the plurality of sets of augmented images, where each augmented image of a set of augmented images is assigned same output label data based on a corresponding one of the training set of medical scans. Inference data is generated by performing an inference function on a new medical scan by utilizing the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.

公开(公告)号：US11829914B2

公开(公告)日：2023-11-28

申请号：US17680493

申请日：2022-02-25

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Anthony Upton ,  Li Yao ,  Jordan Prosky ,  Eric C. Poblenz ,  Chris Croswhite ,  Ben Covington

IPC: G16H30/20 ,  G06Q10/0631 ,  G16H10/60 ,  G16H30/40 ,  G16H15/00 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G16H10/20 ,  G06N5/045 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  H04L67/01 ,  G06V10/82 ,  G06F18/40 ,  G06F18/214 ,  G06F18/21 ,  G06F18/2115 ,  G06F18/2415 ,  G06V10/25 ,  G06V30/19 ,  G06V10/764 ,  G06V40/16 ,  G06V10/22 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06F18/24 ,  G06F18/2111 ,  G06V30/194

CPC classification number: G06Q10/06315 ,  A61B5/7264 ,  G06F3/0482 ,  G06F3/0484 ,  G06F9/542 ,  G06F16/245 ,  G06F18/214 ,  G06F18/217 ,  G06F18/2115 ,  G06F18/2415 ,  G06F18/41 ,  G06F21/6254 ,  G06N5/04 ,  G06N5/045 ,  G06N20/00 ,  G06N20/20 ,  G06Q20/14 ,  G06T3/40 ,  G06T5/002 ,  G06T5/008 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/10 ,  G06T7/11 ,  G06T7/187 ,  G06T7/44 ,  G06T7/97 ,  G06T11/001 ,  G06T11/006 ,  G06T11/206 ,  G06V10/225 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/171 ,  G16H10/20 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/20 ,  H04L67/01 ,  H04L67/12 ,  A61B5/055 ,  A61B6/032 ,  A61B6/5217 ,  A61B8/4416 ,  G06F18/2111 ,  G06F18/24 ,  G06F40/295 ,  G06Q50/22 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30008 ,  G06T2207/30016 ,  G06T2207/30061 ,  G06V30/194 ,  G06V2201/03 ,  G16H50/30 ,  G16H50/70

Abstract: A medical scan header standardization system is operable to determine a plurality of counts for a plurality of entries of at least one of a standard set of fields for headers of a plurality of medical images. A standard set of header entries is determined for at least one of the standard set of fields based on including ones of the entries for the each of the standard set of fields with counts of the plurality of counts that compare favorably to a threshold. One of the standard set of header entries is selected to replace an entry of a field of a header of a medical image. A computer vision model is trained utilizing a training set of images that includes the medical image and the selected one of the standard set of header entries. Inference data for at least one new medical scan is generated based on utilizing the computer vision model.

公开(公告)号：US11790297B2

公开(公告)日：2023-10-17

申请号：US17573184

申请日：2022-01-11

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Li Yao ,  Eric C. Poblenz ,  Jordan Prosky ,  Ben Covington ,  Anthony Upton ,  Lionel Lints

IPC: G16H50/20 ,  G06Q10/0631 ,  G16H10/60 ,  G16H30/40 ,  G16H15/00 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G16H10/20 ,  G06N5/045 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  H04L67/01 ,  G06V10/82 ,  G06F18/40 ,  G06F18/214 ,  G06F18/21 ,  G06F18/2115 ,  G06F18/2415 ,  G06V10/25 ,  G06V30/19 ,  G06V10/764 ,  G06V40/16 ,  G06V10/22 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06F18/24 ,  G06F18/2111 ,  G06V30/194

CPC classification number: G06Q10/06315 ,  A61B5/7264 ,  G06F3/0482 ,  G06F3/0484 ,  G06F9/542 ,  G06F16/245 ,  G06F18/214 ,  G06F18/217 ,  G06F18/2115 ,  G06F18/2415 ,  G06F18/41 ,  G06F21/6254 ,  G06N5/04 ,  G06N5/045 ,  G06N20/00 ,  G06N20/20 ,  G06Q20/14 ,  G06T3/40 ,  G06T5/002 ,  G06T5/008 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/10 ,  G06T7/11 ,  G06T7/187 ,  G06T7/44 ,  G06T7/97 ,  G06T11/001 ,  G06T11/006 ,  G06T11/206 ,  G06V10/225 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/171 ,  G16H10/20 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/20 ,  H04L67/01 ,  H04L67/12 ,  A61B5/055 ,  A61B6/032 ,  A61B6/5217 ,  A61B8/4416 ,  G06F18/2111 ,  G06F18/24 ,  G06F40/295 ,  G06Q50/22 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30008 ,  G06T2207/30016 ,  G06T2207/30061 ,  G06V30/194 ,  G06V2201/03 ,  G16H50/30 ,  G16H50/70

Abstract: A model-assisted annotating system is operable to receive a first set of annotation data, corresponding to a broad type of annotation data output. A first training step is performed to train a computer vision model using the first set of annotation data. A second set of annotation data corresponding to the broad type of annotation data output is generated performing an inference function utilizing the computer vision model on medical scans. Additional annotation data further specifies the broad type of annotation data output is received. A second training step is performed to generate an updated computer vision model using set of additional annotation data. A third set of annotation data corresponding to the specified type of annotation data output is generated by performing an updated inference function utilizing the updated computer vision model on medical scans.

公开(公告)号：US20220084642A1

公开(公告)日：2022-03-17

申请号：US17457050

申请日：2021-12-01

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Li Yao ,  Eric C. Poblenz ,  Jordan Prosky ,  Ben Covington ,  Anthony Upton

IPC: G16H10/60 ,  H04L29/06 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  G06K9/20 ,  H04L29/08

Abstract: A medical scan triaging system is operable to train a computer vision model and to generate abnormality data indicating abnormality probabilities for medical scans via the computer vision model. A first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a first probability value of a triage probability threshold. A second subset of medical scans is determined by identifying medical scans with abnormality probabilities less than the first probability value. An updated first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a second probability value of an updated triage probability threshold. An updated second subset of the plurality of medical scans is determined by identifying medical scans with a abnormality probabilities less than the second probability value. The updated first subset of medical scans is transmitted to client devices.

公开(公告)号：US20210074394A1

公开(公告)日：2021-03-11

申请号：US17100059

申请日：2020-11-20

Applicant: Enlitic, Inc.

Inventor： Jordan Prosky ,  Li Yao ,  Eric C. Poblenz ,  Kevin Lyman ,  Ben Covington ,  Anthony Upton

IPC: G16H10/60 ,  H04L29/06 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  G06K9/20 ,  H04L29/08

Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.

公开(公告)号：US20200160979A1

公开(公告)日：2020-05-21

申请号：US16365787

申请日：2019-03-27

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Li Yao ,  Eric C. Poblenz ,  Jordan Prosky ,  Ben Covington ,  Anthony Upton ,  Lionel Lints

IPC: G16H30/40 ,  G16H50/70 ,  G06K9/62 ,  G06T7/00

Abstract: A model-assisted annotating system is operable to receive a first set of annotation data for a first set of medical scans from a set of client devices. A computer vision model is trained by utilizing first set of medical scans and the first set of annotation data. A second set of annotation data for a second set of medical scans is generated by utilizing the computer vision model. The second set of medical scans and the second set of annotation data is transmitted to the set of client devices, and a set of additional annotation data is received in response. An updated computer vision model is generated by utilizing the set of additional annotation data. A third set of annotation data is generated for a third set of medical scans by utilizing the updated computer vision model for transmission to the set of client devices for display.

公开(公告)号：US20200160970A1

公开(公告)日：2020-05-21

申请号：US16363289

申请日：2019-03-25

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Anthony Upton ,  Li Yao ,  Jordan Prosky ,  Eric C. Poblenz ,  Chris Croswhite ,  Ben Covington

IPC: G16H30/20 ,  G06T7/00 ,  A61B5/00 ,  G06T7/11 ,  G16H10/60

Abstract: A medical scan header standardization system is operable to determine a set of standard DICOM headers based on determining a standard set of fields and based on further determining a standard set of entries for each of the standard set of fields. A DICOM image is received via a network, and a header of the DICOM image is determined to be incorrect. A selected one of the set of standard DICOM headers to replace the header of the DICOM image is determined. The selected one of the set of standard DICOM headers is transmitted, via the network, to a medical scan database for storage in conjunction with the DICOM image.