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1. 发明申请

WO2019212805A1 PHANTOM FOR ADAPTIVE RADIOTHERAPY 审中-公开
公开(公告)号：WO2019212805A1
公开(公告)日：2019-11-07
申请号：PCT/US2019/028724
申请日：2019-04-23
申请人： ELEKTA, INC.
发明人： MAGRO, Nicolette Patricia , HAN, Xiao
IPC分类号： A61N5/10
摘要： A deformable radiotherapy phantom can be produced using an additive manufacturing process, based on a medical image of the patient. The deformable phantom can include dosimeters for measuring radiation dose distribution. A smart material can allow deformation in response to an applied stimulus. Among other things, the phantom can be used to validate radiation dose warping, a radiotherapy treatment plan, to determine a maximum acceptable deformation of the patient, to validate a cumulative accuracy of dose warping and deformable image registration, or the like.

2. 发明申请

WO2019143684A1 METHODS AND DEVICES FOR SURFACE MOTION TRACKING 审中-公开
公开(公告)号：WO2019143684A1
公开(公告)日：2019-07-25
申请号：PCT/US2019/013799
申请日：2019-01-16
申请人： ELEKTA, INC.
发明人： MAGRO, Nicolette Patricia , HAN, Xiao
IPC分类号： G06T7/30
CPC分类号： G06T7/38 , A61B5/0077 , A61B5/015 , A61B5/055 , A61B6/032 , A61B6/4085 , A61B2090/364 , A61N5/1039 , G06T7/30 , G06T2207/10004 , G06T2207/10021 , G06T2207/10048 , G06T2207/10081 , G06T2207/10088 , G06T2207/10108 , G06T2207/20221 , G06T2207/30004
摘要： Embodiments of the disclosure may be directed to an image processing system configured to receive a medical image of a region of a subject's body taken at a first time and to receive a surface image of an exterior portion of the region of the subject's body taken at the first time. The image processing may also be configured to receive a medical image of the region of the subject's body taken at a second time and to register the medical image taken at the first time, the surface image taken at the first time, and the medical image taken at the second time.

3. 发明申请

WO2019199699A1 IMAGE SYNTHESIS USING ADVERSARIAL NETWORKS 审中-公开
公开(公告)号：WO2019199699A1
公开(公告)日：2019-10-17
申请号：PCT/US2019/026400
申请日：2019-04-08
申请人： ELEKTA, INC.
发明人： HAN, Xiao
IPC分类号： G06K9/62
摘要： A statistical learning technique that does not rely upon paired imaging information is described herein. The technique may be computer-implemented and may be used in order to train a statistical learning model to perform image synthesis, such as in support of radiation therapy treatment planning. In an example, a trained statistical learning model may include a convolutional neural network established as a generator convolutional network, and the generator may be trained at least in part using a separate convolutional neural network established as a discriminator convolutional network. The generator convolutional network and the discriminator convolutional network may form an adversarial network architecture for use during training. After training, the generator convolutional network may be provided for use in synthesis of images, such as to receive imaging data corresponding to a first imaging modality type, and to synthesize imaging data corresponding to a different, second imaging modality type.

4. 发明申请

WO2019005180A1 METHOD FOR IMPROVING CONE-BEAM CT IMAGE QUALITY USING A DEEP CONVOLUTIONAL NEURAL NETWORK 审中-公开
公开(公告)号：WO2019005180A1
公开(公告)日：2019-01-03
申请号：PCT/US2017/043479
申请日：2017-07-24
申请人： ELEKTA, INC.
发明人： XU, Jiaofeng , HAN, Xiao
IPC分类号： G06T11/00 , G06T5/00
摘要： Systems and methods can include training a DCNN to reduce one or more artifacts using a projection space approach or an image space approach. A projection space approach can include collecting an artifact contaminated CBCT projection space image, and a corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated CBCT projection space image and the corresponding artifact reduced, CBCT projection space image collected from each patient in the group of patients to train a DCNN to reduce one or more artifacts in a projection space image. An image space approach can include collecting a plurality of CBCT patient anatomical images and corresponding registered CT anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced CT anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.

5. 发明申请

WO2018039368A1 IMAGE SEGMENTATION USING NEURAL NETWORK METHOD 审中-公开
标题翻译：用神经网络方法分割图像
公开(公告)号：WO2018039368A1
公开(公告)日：2018-03-01
申请号：PCT/US2017/048245
申请日：2017-08-23
申请人： ELEKTA, INC.
发明人： XU, Jiaofeng , HAN, Xiao
IPC分类号： G06T7/11
摘要： The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving a three-dimensional image acquired by an imaging device, and selecting a plurality of stacks of adjacent two-dimensional images from the three-dimensional image. The method may further include segmenting, by a processor, each stack of adjacent two-dimensional images using a neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the plurality of stacks.
摘要翻译：本公开涉及用于分割三维图像的系统，方法，设备和非临时性计算机可读存储介质。在一个实现中，提供了用于分割三维图像的计算机实现的方法。该方法可以包括接收由成像装置获取的三维图像，并且从三维图像中选择多个相邻二维图像的堆叠。该方法可以进一步包括由处理器使用神经网络模型来分割相邻二维图像的每个堆叠。该方法还可以包括通过聚集来自多个堆叠的分割结果来由处理器确定三维图像的标签图。

6. 发明申请

WO2019209820A1 IMAGE ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORKS 审中-公开
公开(公告)号：WO2019209820A1
公开(公告)日：2019-10-31
申请号：PCT/US2019/028710
申请日：2019-04-23
申请人： ELEKTA, INC.
发明人： XU, Jiaofeng , HAN, Xiao
IPC分类号： G06T11/00
摘要： Techniques for generating an enhanced cone-beam computed tomography (CBCT) image using a trained model are provided. A CBCT image of a subject is received. a synthetic computed tomography (sCT) image corresponding to the CBCT image is generated, using a generative model. The generative model is trained in a generative adversarial network (GAN). The generative model is further trained to process the CBCT image as an input and provide the sCT image as an output. The sCT image is presented for medical analysis of the subject.

7. 发明申请

WO2019160850A1 ATLAS-BASED SEGMENTATION USING DEEP-LEARNING 审中-公开
公开(公告)号：WO2019160850A1
公开(公告)日：2019-08-22
申请号：PCT/US2019/017626
申请日：2019-02-12
申请人： ELEKTA, INC.
发明人： HAN, Xiao , MAGRO, Nicolette Patricia
IPC分类号： G06T7/11 , G06T7/174
摘要： Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.

8. 发明申请

WO2018118373A1 ONLINE LEARNING ENHANCED ATLAS-BASED AUTO-SEGMENTATION 审中-公开
公开(公告)号：WO2018118373A1
公开(公告)日：2018-06-28
申请号：PCT/US2017/063964
申请日：2017-11-30
申请人： ELEKTA, INC.
发明人： HAN, Xiao
IPC分类号： G06T7/11
摘要： An image segmentation method is disclosed. The method includes receiving a plurality of atlases and a subject image, each atlas including an atlas image showing a structure of interest and associated structure delineations, the subject image being acquired by an image acquisition device and showing the structure of interest. The method further includes calculating, by an image processor, mapped atlases by registering the respective atlases to the subject image, and determining, by the image processor, a first structure label map for the subject image based on the mapped atlases. The method also includes training, by the image processor, a structure classifier using a subset of the mapped atlases, and determining, by the image processor, a second structure label map for the subject image by applying the trained structure classifier to one or more subject image points in the subject image. The method additional includes combining, by the image processor, the first label map and the second label map to generate a third label map representative of the structure of interest.

9. 发明申请

WO2018039380A1 SYSTEMS AND METHODS FOR IMAGE SEGMENTATION USING CONVOLUTIONAL NEURAL NETWORK 审中-公开
标题翻译：用卷积神经网络进行图像分割的系统和方法
公开(公告)号：WO2018039380A1
公开(公告)日：2018-03-01
申请号：PCT/US2017/048271
申请日：2017-08-23
申请人： ELEKTA, INC.
发明人： XU, Jiaofeng , HAN, Xiao
IPC分类号： G06T7/00 , G06T7/11 , G06T7/174
摘要： The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack.
摘要翻译：本公开涉及用于分割三维图像的系统，方法，设备和非临时性计算机可读存储介质。在一个实现中，提供了用于分割三维图像的计算机实现的方法。该方法可以包括接收由成像装置获取的三维图像，并且从三维图像的第一平面创建第一堆二维图像，以及从第二平面创建二维图像堆三维图像。该方法还可以包括由处理器使用至少一个神经网络模型来分割第一堆栈和第二堆二维图像。该方法还可以包括通过聚集来自第一堆叠和第二堆叠的分割结果来由处理器确定三维图像的标签图。

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