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公开(公告)号：US12045988B2
公开(公告)日：2024-07-23
申请号：US17214348
申请日：2021-03-26
发明人： Esa Kuusela , Hannu Laaksonen
CPC分类号： G06T7/11 , G06N20/00 , G16H20/40 , G16H30/20 , G06T2207/20081
摘要： Disclosed herein are systems and methods for training a machine learning model for automatic organ segmentation. A processor receives an image of one or more pre-contoured organs, the image comprising a plurality of voxels. The processor executes a machine learning model using the image to output predicted organ labels for the plurality of voxels of the image. The processor determines differences between corresponding predicted organ labels and expected organ labels for the plurality of voxels. The processor determines radiation dose levels that correspond to the plurality of voxels of the image. The processor determines weights for the plurality of voxels based on the radiation dose levels of the respective voxels. The processor then trains the machine learning model based on the differences and the weights for the plurality of voxels.
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公开(公告)号：US12119102B1
公开(公告)日：2024-10-15
申请号：US17530383
申请日：2021-11-18
发明人： Heini Hyvonen , Hannu Laaksonen , Ville Pietila
CPC分类号： G16H30/40 , A61N5/1064 , G06T7/0012 , G16H50/20 , A61N2005/1074 , G06T2200/24 , G06T2207/20081 , G06T2207/30004
摘要： Disclosed herein are methods and systems for predicting a virtual bolus in order to generate a radiation therapy treatment plan comprising training, by a processor, a machine-learning model using a training dataset comprising a set of medical images corresponding to a set of previously performed radiation therapy treatments, each medical image comprising at least one planning target volume and a non-anatomical region added to the medical image; and executing, by the processor, the machine-learning model using a medical image not included within the training dataset, the machine-learning model predicting an attribute of a non-anatomical region for the medical image not included in the training dataset.
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3.发明授权公开(公告)号：US12033322B2
公开(公告)日：2024-07-09
申请号：US17557725
申请日：2021-12-21
发明人： Hannu Laaksonen , Janne Nord , Jan Schreier
CPC分类号： G06T7/0012 , G06N3/08 , G06T7/11 , G06V10/25 , G06V10/82 , A61N5/1047 , G06T7/174 , G06T2207/10072 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221 , G06T2207/30096 , G06V10/454
摘要： One or more medical images of a patient are processed by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop the medical images, which serves as input to a second neural network model. The second neural network model processes the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.
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公开(公告)号：US20240001139A1
公开(公告)日：2024-01-04
申请号：US17854270
申请日：2022-06-30
发明人： Esa Kuusela , Mikko Hakala , María Isabel Cordero-Marcos , Elena Czeizler , Shahab Basiri , Hannu Laaksonen
CPC分类号： A61N5/045 , G06N20/00 , A61N5/1031
摘要： A control circuit accesses a plurality of information items that each correspond to a resultant dose volume histogram shape for a corresponding different radiation treatment plan. The control circuit then trains a machine learning model to predict a desired dose volume histogram shape using that plurality of information items as a training corpus.
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5.发明授权公开(公告)号：US11682485B2
公开(公告)日：2023-06-20
申请号：US17953346
申请日：2022-09-27
发明人： Hannu Laaksonen , Janne Nord , Sami Petri Perttu
CPC分类号： G16H30/40 , A61N5/1031 , A61N5/1038 , A61N5/1039 , G06N3/08 , G16B40/00 , G16H20/40 , A61N2005/1041
摘要： Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.
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公开(公告)号：US20240001138A1
公开(公告)日：2024-01-04
申请号：US17853454
申请日：2022-06-29
发明人： Mikko Hakala , Esa Kuusela , Elena Czeizler , Shahab Basiri , María Isabel Cordero-Marcos , Hannu Laaksonen , Alexander E. Maslowski
CPC分类号： A61N5/045 , G09G3/00 , A61N5/1031
摘要： A control circuit accesses a radiation treatment plan for a given patient. The control circuit then generates dose volume histogram information as a function of the radiation treatment plan and automatically assesses the dose volume histogram information to identify any anomalous results. Generating that information can comprise, at least in part and for example, generating at least one dose volume histogram curve. The latter may comprise generating at least one dose volume histogram curve for each of a plurality of different patient structures (such as one or more treatment volumes and/or one or more organs-at-risk).
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公开(公告)号：US11654299B2
公开(公告)日：2023-05-23
申请号：US16919746
申请日：2020-07-02
CPC分类号： A61N5/1031 , A61N5/10 , A61N5/103 , A61N5/1042 , A61N5/1043 , A61N5/1045 , A61N5/1047 , A61N5/1077 , A61N5/1081 , G16H20/40 , A61N2005/1041
摘要： A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm. By one approach the control circuit can be configured to, when presenting automatically-generated radiation treatment plans to a user, also co-present an opportunity for the user to signal to a remote entity that none of the plans are acceptable and that the user will instead employ a user-generated plan for the particular patient.
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