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1. 发明专利

GB2567155B Histopathological image analysis 未知
公开(公告)号：GB2567155B
公开(公告)日：2022-03-02
申请号：GB201716060
申请日：2017-10-02
申请人： ROOM4 GROUP LTD
发明人： JOHN ROBERT MADDISON , HAVARD DANIELSEN
IPC分类号： G06T7/00 , A61B5/00 , G06T7/33
摘要： An apparatus and computer-implemented method for training a machine-learning algorithm to perform histopathological analysis is disclosed. The method comprises obtaining (210) a plurality of first microscopic images of first histological specimens that have been stained with a first marker; and obtaining (212), a respective plurality of second microscopic images of second histological specimens that have been stained with a second, different marker. The method further comprises obtaining (220) a respective plurality of mask images generated for the second microscopic images, each mask image identifying a histological feature of interest highlighted in the respective second microscopic image by the second marker. The method comprises training (240) the machine-learning algorithm to predict, from a first microscopic image, a histological feature of interest that would be highlighted in the same specimen by the second marker. Also disclosed is an apparatus and computer-implemented method for histopathological analysis using the trained machine-learning algorithm.

2. 发明专利

GB2486398A Method of classifying cell nuclei using binary boosted classifiers 未知
公开(公告)号：GB2486398A
公开(公告)日：2012-06-20
申请号：GB201019429
申请日：2010-11-17
申请人： ROOM4 GROUP LTD
发明人： MADDISON JOHN ROBERT , DANIELSEN HAVARD EMIL
IPC分类号： G06K9/00 , G06K9/62
摘要： The invention relates to a system for classifying objects in biological specimens. Images of biological tissue are captured using a microscope 3 connected to a camera 1. These images are then segmented and classified using a supervised learning method to identify different nuclei types and to reject artefacts that arise from the segmentation process. The method employs two binary classifiers with boosting. The classifiers are arranged in a cascade system. The classification of different types of cell nuclei and the rejection of artefacts is thus achieved in a completely automatic way. The nuclei may be characterised by parameters such as dimensions, shape, area, Hu moments, jaggedness, and number of grey levels in the image. The boosting method may be Adaboost or Gentleboost. The classifier may be first trained using a training database.

3. 发明专利

SG11202106313XA CLASSIFICATION OF CELL NUCLEI 未知
公开(公告)号：SG11202106313XA
公开(公告)日：2021-07-29
申请号：SG11202106313X
申请日：2019-11-07
申请人： ROOM4 GROUP LTD
发明人： MADDISON JOHN ROBERT , DANIELSEN HÅVARD
IPC分类号： G06K9/62 , G06K9/00
摘要： The present invention relates to a system that can be used to accurately classify objects in biological specimens. The user firstly classifies manually an initial set of images, which are used to train a classifier. The classifier then is run on a complete set of images, and outputs not merely the classification but the probability that each image is in a variety of classes. Images are then displayed, sorted not merely by the proposed class but also the likelihood that the image in fact belongs in a proposed alternative class. The user can then reclassify images as required.

4. 发明专利

GB2567155A Histopathological image analysis 未知
公开(公告)号：GB2567155A
公开(公告)日：2019-04-10
申请号：GB201716060
申请日：2017-10-02
申请人： ROOM4 GROUP LTD
发明人： JOHN ROBERT MADDISON , HAVARD DANIELSEN
IPC分类号： G06T7/00 , A61B5/00 , G06T7/33
摘要： The invention relates to an apparatus and a computer-implemented method for training a machine‑learning algorithm to perform histopathological analysis. The method comprises obtaining 210 a plurality of first microscopic images of first histological specimens that have been stained with a first marker, followed by the scanning 212 of a related group of second microscopic images of second histological samples that have been stained with a different dye. The method further comprises obtaining 220 a respective plurality of mask images (Figs. 12, 13) generated for the second microscopic images, each mask identifying a histological feature of interest tinted in the associated second image by the second marker. The technique also comprises training 240 the machine-learning algorithm to predict, from a first microscopic image, a histological feature of interest that would be highlighted in the same specimen by the second marker. A region of interest (ROI) may be selected in each first image and a third marker may be used. The first marker may comprise haemotoxylin, eosin and a first immunohistochemical marker, while the second marker may comprise a second immunohistochemical marker. All markers may be fluorescent. The machine-learning algorithm may be a neural network.

5. 发明专利

GB201716060D0 Histopathological image analysis 未知
公开(公告)号：GB201716060D0
公开(公告)日：2017-11-15
申请号：GB201716060
申请日：2017-10-02
申请人： ROOM4 GROUP LTD
摘要： An apparatus and computer-implemented method for training a machine-learning algorithm to perform histopathological analysis is disclosed. The method comprises obtaining (210) a plurality of first microscopic images of first histological specimens that have been stained with a first marker; and obtaining (212), a respective plurality of second microscopic images of second histological specimens that have been stained with a second, different marker. The method further comprises obtaining (220) a respective plurality of mask images generated for the second microscopic images, each mask image identifying a histological feature of interest highlighted in the respective second microscopic image by the second marker. The method comprises training (240) the machine-learning algorithm to predict, from a first microscopic image, a histological feature of interest that would be highlighted in the same specimen by the second marker. Also disclosed is an apparatus and computer-implemented method for histopathological analysis using the trained machine-learning algorithm.

6. 发明专利

GB2466948A Push messaging for images to mobiles 未知
公开(公告)号：GB2466948A
公开(公告)日：2010-07-21
申请号：GB0900541
申请日：2009-01-14
申请人： MADDISON JOHN ROBERT , ROOM4 GROUP LTD
发明人： MADDISON JOHN ROBERT , DANIELSEN HAVARD EMIL
IPC分类号： G06F19/00 , G06Q10/00
摘要： Push messages are sent to clients mobile devices, such as smart phones, so that large microscopy images can be viewed for a case to be diagnosed, and stored by a central server may be accessed, by selecting areas of the image. The resulting work flow system allows for the efficient review of large microscopy scans. The system included use of multi-touch technology for navigation of microscopy image and messages may be sent to the pathologist using sms to inform the user that there is a case available, so that the user can access an image server and provide a diagnosis of the medical condition.

7. 发明专利

GB2579797B Classification of cell nuclei 未知
公开(公告)号：GB2579797B
公开(公告)日：2022-11-16
申请号：GB201820361
申请日：2018-12-13
申请人： ROOM4 GROUP LTD
发明人： JOHN ROBERT MADDISON , HAVARD DANIELSEN
IPC分类号： G06V20/69 , G06T7/00
摘要： The present invention relates to a system that can be used to accurately classify objects in biological specimens. The user firstly classifies manually an initial set of images, which are used to train a classifier. The classifier then is run on a complete set of images, and outputs not merely the classification but the probability that each image is in a variety of classes. Images are then displayed, sorted not merely by the proposed class but also the likelihood that the image in fact belongs in a proposed alternative class. The user can then reclassify images as required.

8. 发明专利

GB201820361D0 Classification of cell nuclei 未知
公开(公告)号：GB201820361D0
公开(公告)日：2019-01-30
申请号：GB201820361
申请日：2018-12-13
申请人： ROOM4 GROUP LTD
摘要： The present invention relates to a system that can be used to accurately classify objects in biological specimens. The user firstly classifies manually an initial set of images, which are used to train a classifier. The classifier then is run on a complete set of images, and outputs not merely the classification but the probability that each image is in a variety of classes. Images are then displayed, sorted not merely by the proposed class but also the likelihood that the image in fact belongs in a proposed alternative class. The user can then reclassify images as required.

9. 发明专利

GB2466818A Cell image segmentation using binary threshold and greyscale image processing 未知
公开(公告)号：GB2466818A
公开(公告)日：2010-07-14
申请号：GB0900248
申请日：2009-01-09
申请人： MADDISON JOHN R , ROOM4 GROUP LTD
发明人： MADDISON JOHN R , DANIELSEN HAVARD EMIL , NIELSEN BIRGITTE
IPC分类号： G06T7/00
摘要： An image segmentation method comprises: thresholding a greyscale image of plural nuclei to create a black and white image; post processing the B/W image to remove objects failing to meet criteria based on the B/W image and greyscale image (e.g. calculating the gradient in the greyscale image at boundaries identified in the B/W image and comparing this with a gradient threshold); segmenting to extract objects corresponding to objects remaining after this processing; and further using edge detection on the segmented image, to determine edges of real nuclei. Objects with areas less than a predetermined number of pixels may be removed. Grey scale images may be smoothed via Gaussian filtering, and convolved with the derivative of a Gaussian, prior to gradient calculation. A Canny edge detector and gradient vector flow snake may be used for edge detection. The invention relates to optimizing the initialization and convergence of active contours (or 'snakes') for segmentation of cell nuclei into objects and holes in histological sections.

10. 发明专利

GB201019429D0 Cell classification and artefact rejection for cell nuclei 未知
公开(公告)号：GB201019429D0
公开(公告)日：2010-12-29
申请号：GB201019429
申请日：2010-11-17
申请人： ROOM4 GROUP LTD
摘要： The invention relates to a system for classifying objects in biological specimens. Images of biological tissue are captured using a microscope 3 connected to a camera 1. These images are then segmented and classified using a supervised learning method to identify different nuclei types and to reject artefacts that arise from the segmentation process. The method employs two binary classifiers with boosting. The classifiers are arranged in a cascade system. The classification of different types of cell nuclei and the rejection of artefacts is thus achieved in a completely automatic way. The nuclei may be characterised by parameters such as dimensions, shape, area, Hu moments, jaggedness, and number of grey levels in the image. The boosting method may be Adaboost or Gentleboost. The classifier may be first trained using a training database.

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