Su Yen Wang

8345976, Jan 1, 2013

Aug 6, 2010

12/852096

Su Wang - San Jose CA, US
Shengyang Dai - San Jose CA, US
Akira Nakamura - Cupertino CA, US
Takeshi Ohashi - Kanagawa, JP
Jun Yokono - Tokyo, JP

Sony Corporation - Tokyo

G06K 9/34

382173, 382128, 382154

Methods and systems disclosed herein provide the capability to automatically process digital pathology images quickly and accurately. According to one embodiment, an digital pathology image segmentation task may be divided into at least two parts. An image segmentation task may be carried out utilizing both bottom-up analysis to capture local definition of features and top-down analysis to use global information to eliminate false positives. In some embodiments, an image segmentation task is carried out using a “pseudo-bootstrapping” iterative technique to produce superior segmentation results. In some embodiments, the superior segmentation results produced by the pseudo-bootstrapping method are used as input in a second segmentation task that uses a combination of bottom-up and top-down analysis.

8351676, Jan 8, 2013

Oct 12, 2010

12/902321

Shengyang Dai - San Jose CA, US
Su Wang - San Jose CA, US
Akira Nakamura - San Jose CA, US
Takeshi Ohashi - Kanagawa, JP
Jun Yokono - Tokyo, JP

Sony Corporation - Tokyo

G06K 9/00
G06K 9/34

382133, 382128, 382134, 382173

Systems and methods for implementing a multi-step image recognition framework for classifying digital images are provided. The provided multi-step image recognition framework utilizes a gradual approach to model training and image classification tasks requiring multi-dimensional ground truths. A first step of the multi-step image recognition framework differentiates a first image region from a remainder image region. Each subsequent step operates on a remainder image region from the previous step. The provided multi-step image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-step image recognition frameworks.

8379994, Feb 19, 2013

Oct 13, 2010

12/904138

Shengyang Dai - San Jose CA, US
Su Wang - San Jose CA, US
Akira Nakamura - San Jose CA, US
Takeshi Ohashi - Kanagawa, JP
Jun Yokono - Tokyo, JP

Sony Corporation - Tokyo

G06K 9/62

382224, 382158, 382159, 382160, 382161

Systems and methods for implementing a multi-label image recognition framework for classifying digital images are provided. The provided multi-label image recognition framework utilizes an iterative, multiple analysis path approach to model training and image classification tasks. A first iteration of the multi-label image recognition framework generates confidence maps for each label, which are shared by the multiple analysis paths to update the confidence maps in subsequent iterations. The provided multi-label image recognition framework permits model training and image classification tasks to be performed more accurately than conventional single-label image recognition frameworks.

8588518, Nov 19, 2013

Nov 22, 2010

12/951702

Su Wang - San Jose CA, US
Shengyang Dai - San Jose CA, US
Akira Nakamura - San Jose CA, US
Takeshi Ohashi - Kanagawa, JP
Jun Yokono - Tokyo, JP

Sony Corporation - Tokyo

G06K 9/62

382159

Systems and methods for implementing a superpixel boosted top-down image recognition framework are provided. The framework utilizes superpixels comprising contiguous pixel regions sharing similar characteristics. Feature extraction methods described herein provide non-redundant image feature vectors for classification model building. The provided framework differentiates a digitized image into a plurality of superpixels. The digitized image is characterized through image feature extraction methods based on the plurality of superpixels. Image classification models are generated from the extracted image features and ground truth labels and may then be used to classify other digitized images.

8605973, Dec 10, 2013

Mar 17, 2012

13/423211

Su Wang - San Jose CA, US
Shengyang Dai - San Jose CA, US
Xun Xu - Palo Alto CA, US
Akira Nakamura - San Jose CA, US

Sony Corporation - Tokyo

G06K 9/00

382128, 128922, 378 4

An interactive segmentation framework for 3-D teeth CT volumetric data enables a user to segment an entire dental region or individual teeth depending upon the types of user input. Graph cuts-based interactive segmentation utilizes a user's scribbles which are collected on several 2-D representative CT slices and are expanded on those slices. Then, a 3-D distance transform is applied to the entire CT volume based on the expanded scribbles. Bony tissue enhancement is added before feeding 3-D CT raw image data into the graph cuts pipeline. The segmented teeth area is able to be directly utilized to reconstruct a 3-D virtual teeth model.

20120033861, Feb 9, 2012

Aug 6, 2010

12/851818

Shengyang Dai - San Jose CA, US
Su Wang - San Jose CA, US
Akira Nakamura - Cupertino CA, US
Takeshi Ohashi - Kanagawa, JP
Jun Yokono - Tokyo, JP

G06K 9/00

382128

Systems and methods for implementing a hierarchical image recognition framework for classifying digital images are provided. The provided hierarchical image recognition framework utilizes a multi-layer approach to model training and image classification tasks. A first layer of the hierarchical image recognition framework generates first layer confidence scores, which are utilized by the second layer to produce a final recognition score. The provided hierarchical image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-layer image recognition frameworks. In some embodiments real-time operator guidance is provided for an image classification task.

20130243277, Sep 19, 2013

Mar 17, 2012

13/423208

Su Wang - San Jose CA, US
Xun Xu - Palo Alto CA, US
Akira Nakamura - San Jose CA, US

SONY CORPORATION - Tokyo

G06K 9/00

382128

Fluorescence in situ hybridization (FISH) enables the detection of specific DNA sequences in cell chromosomes by the use of selective staining. Due to the high sensitivity, FISH allows the use of multiple colors to detect multiple targets simultaneously. The target signals are represented as colored dots, and enumeration of these signals is called dot counting. Using a two-stage segmentation framework guarantees locating all potential dots including overlapped dots.

20130243308, Sep 19, 2013

Mar 17, 2012

13/423209

Su Wang - San Jose CA, US
Shengyang Dai - San Jose CA, US
Xun Xu - Palo Alto CA, US
Akira Nakamura - San Jose CA, US
Takeshi Ohashi - Kanagawa, JP
Jun Yokono - Tokyo, JP

SONY CORPORATION - Tokyo

G06K 9/62

382159

An integrated interactive segmentation with spatial constraint method utilizes a combination of several of the most popular online learning algorithms into one and implements a spatial constraint which defines a valid mask local to the user's given marks. Additionally, both supervised learning and statistical analysis are integrated, which are able to compensate each other. Once prediction and activation are obtained, pixel-wised multiplication is conducted to fully indicate how likely each pixel belongs to the foreground or background.