公开(公告)号：US20220148286A1

公开(公告)日：2022-05-12

申请号：US17581836

申请日：2022-01-21

Applicant: FUJIFILM Corporation

Inventor： Deepak KESHWANI

IPC: G06V10/426 ,  G06V10/774 ,  G06V10/98 ,  G06V10/82

Abstract: A learning unit derives, from a target image including at least one tubular structure, in a case where an image for learning and ground-truth data of a graph structure included in the image for learning are input to an extraction model which extracts a feature vector of a plurality of nodes constituting a graph structure of the tubular structure, a loss between nodes on the graph structure included in the image for learning on the basis of an error between a feature vector distance between nodes belonging to the same graph structure and a topological distance which is a distance on a route of the graph structure between the nodes, and performs learning of the extraction model on the basis of the loss.

公开(公告)号：US20220114393A1

公开(公告)日：2022-04-14

申请号：US17556983

申请日：2021-12-20

Applicant: FUJIFILM Corporation

Inventor： Deepak KESHWANI

IPC: G06K9/62 ,  G06V10/82 ,  G06V10/764 ,  G06N3/04 ,  G16H30/40 ,  G16H50/20

Abstract: A first learning unit performs learning of a first neural network that extracts a feature vector in each pixel of a target image including a plurality of objects and that outputs a feature map in which feature vectors of pixels belonging to individual objects included in the target image are clustered and distributed as a plurality of the feature vector groups in a feature space which is a space of the feature vector. A second learning unit performs learning of a second neural network that outputs a class classification result of a plurality of objects belonging to the same category included in the target image in response to input of the feature vector of the target image.

公开(公告)号：US20210271914A1

公开(公告)日：2021-09-02

申请号：US17326349

申请日：2021-05-21

Applicant: FUJIFILM Corporation

Inventor： Deepak KESHWANI

IPC: G06K9/03 ,  G06T7/11 ,  G06K9/62 ,  G06N20/00 ,  G16H30/40

Abstract: Provided are an image processing apparatus, an image processing method, and a program that can reduce the time and effort required to correct the segmentation of a medical image. An image processing apparatus includes: an image acquisition unit (40) that acquires a medical image (200); a segmentation unit (42) that performs segmentation on the medical image acquired by the image acquisition unit and classifies the medical image into prescribed classes for each local region; a global feature acquisition unit (46) that acquires a global feature indicating an overall feature of the medical image; and a correction unit (44) that corrects a class of a correction target region that is a local region whose class is to be corrected in the medical image according to the global feature with reference to a relationship between the global feature and the class.

公开(公告)号：US20240193785A1

公开(公告)日：2024-06-13

申请号：US18587853

申请日：2024-02-26

Applicant: FUJIFILM Corporation

Inventor： Kiyoshi HASEGAWA ,  Yusuke KAZAMI ,  Junichi KANEKO ,  Deepak KESHWANI

IPC: G06T7/11 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V20/70 ,  G16H30/40

CPC classification number: G06T7/11 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V20/70 ,  G16H30/40 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30056 ,  G06T2207/30101 ,  G06V2201/031

Abstract: A medical image processing apparatus employs a trained model generated by performing machine learning using training data that includes first input data including a first image regarding a liver, and portal vein branch labeling data in which a portal vein branch label is attached to a portal vein region in the liver in the first image for each portal vein branch corresponding to a hepatic segment. The medical image processing apparatus uses the trained model to assign the portal vein branch label to each image unit element of a second image region including at least a liver region of the second image included in a second input data which is the same type as the first input data, and divides the liver region included in the second input data into hepatic segments based on the portal vein branch label assigned to each image unit element of the second image region.

公开(公告)号：US20210272290A1

公开(公告)日：2021-09-02

申请号：US17326340

申请日：2021-05-21

Applicant: FUJIFILM Corporation

Inventor： Deepak KESHWANI ,  Yoshiro KITAMURA

IPC: G06T7/11 ,  G16H30/40 ,  G06N3/04 ,  G06T5/00 ,  G06T5/20

Abstract: Provided are an image processing apparatus, an image processing method, and a program that can suppress an error in the segmentation of a medical image. An image processing apparatus includes: a segmentation unit (42) that applies deep learning to perform segmentation which classifies a medical image (200) into a specific class on the basis of a local feature of the medical image; and a global feature classification unit (46) that applies deep learning to classify the medical image into a global feature which is an overall feature of the medical image. The segmentation unit shares a weight of a first low-order layer which is a low-order layer with a second low-order layer which is a low-order layer in the global feature classification unit.

公开(公告)号：US20200380313A1

公开(公告)日：2020-12-03

申请号：US16996871

申请日：2020-08-18

Applicant: FUJIFILM Corporation

Inventor： Deepak KESHWANI ,  Yoshiro KITAMURA

IPC: G06K9/62 ,  G06T7/00 ,  G06K9/66

Abstract: Provided is a machine learning device and method that enables machine learning of labeling, in which a plurality of labels are attached to volume data at one effort with excellent accuracy, using training data having label attachment mixed therein.A probability calculation unit (14) calculates a value (soft label) indicating a likelihood of labeling of a class Ci for each voxel of a second slice image by means of a learned teacher model (13a). A detection unit (15) detects “bronchus” and “blood vessel” for the voxels of the second slice image using a known method, such as a region expansion method and performs labeling of “bronchus” and “blood vessel”. A correction probability setting unit (16) replaces the soft label with a hard label of “bronchus” or “blood vessel” detected by the detection unit (15). A distillation unit (17) performs distillation of a student model (18a) from the teacher model (13a) using the soft label after correction by means of the correction probability setting unit (16). With this, the learned student model (18a) is obtained.