WO2021116909A1 - Method for determining severity of skin disease based on percentage of body surface area covered by lesions - Google Patents

Method for determining severity of skin disease based on percentage of body surface area covered by lesions Download PDF

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Publication number
WO2021116909A1
WO2021116909A1 PCT/IB2020/061648 IB2020061648W WO2021116909A1 WO 2021116909 A1 WO2021116909 A1 WO 2021116909A1 IB 2020061648 W IB2020061648 W IB 2020061648W WO 2021116909 A1 WO2021116909 A1 WO 2021116909A1
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WIPO (PCT)
Prior art keywords
regions
image
training set
segmentation
lesion
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Ceased
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PCT/IB2020/061648
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English (en)
French (fr)
Inventor
Yanqing Chen
Charles Tang
Ernesto J. MUNOZ-ELIAS
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Janssen Biotech Inc
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Janssen Biotech Inc
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Application filed by Janssen Biotech Inc filed Critical Janssen Biotech Inc
Priority to IL293654A priority Critical patent/IL293654A/en
Priority to KR1020227023195A priority patent/KR20220108158A/ko
Priority to EP20899126.5A priority patent/EP4073752A4/en
Priority to AU2020401794A priority patent/AU2020401794A1/en
Priority to CN202080085246.7A priority patent/CN114830173A/zh
Priority to BR112022011111A priority patent/BR112022011111A2/pt
Priority to JP2022534623A priority patent/JP7695243B2/ja
Priority to CA3164066A priority patent/CA3164066A1/en
Publication of WO2021116909A1 publication Critical patent/WO2021116909A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30088—Skin; Dermal
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion

Definitions

  • Step 604 Manually classify each of the proposed regions in each of the plurality of oversegmented training set images as being a lesion or a non-lesion. This step is performed by one or more human classifiers 702 shown in Figure 7A.
  • the proposed regions outputted from the Felzenszwalb segmentation algorithm (with the k-value set to 250) were used to oversegment the image, thereby including as many true positive lesion regions as possible.
  • about 30 guttate psoriasis images were chosen, which generated around 3000 proposed region images.
  • each of the 3000 proposed regions were manually (human) classified as lesion or non-lesion.
  • the process was repeated three times.
  • Some of these non-lesions were easy to identify, including huge regions covering a lot of skin, black background areas, necklaces, noise, and the like.
  • Other regions were harder to distinguish between lesion and non-lesion including regions that had shade, bad lighting, scars, and the like.
  • a large source of error is believed to be attributed to the incongruencies in the dataset and these harder regions to classify as lesion versus non-lesion.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Image Analysis (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
PCT/IB2020/061648 2019-12-09 2020-12-08 Method for determining severity of skin disease based on percentage of body surface area covered by lesions Ceased WO2021116909A1 (en)

Priority Applications (8)

Application Number Priority Date Filing Date Title
IL293654A IL293654A (en) 2019-12-09 2020-12-08 Method for determining severity of skin disease based on percentage of body surface area covered by lesions
KR1020227023195A KR20220108158A (ko) 2019-12-09 2020-12-08 병변으로 덮인 체표면적 백분율에 기초한 피부 질환의 중증도 결정 방법
EP20899126.5A EP4073752A4 (en) 2019-12-09 2020-12-08 Method for determining severity of skin disease based on percentage of body surface area covered by lesions
AU2020401794A AU2020401794A1 (en) 2019-12-09 2020-12-08 Method for determining severity of skin disease based on percentage of body surface area covered by lesions
CN202080085246.7A CN114830173A (zh) 2019-12-09 2020-12-08 基于由病灶覆盖的人体表面积的百分比确定皮肤病的严重程度的方法
BR112022011111A BR112022011111A2 (pt) 2019-12-09 2020-12-08 Método para determinar a gravidade da doença da pele com base na porcentagem de área de superfície corporal coberta por lesões
JP2022534623A JP7695243B2 (ja) 2019-12-09 2020-12-08 病変が占める体表面積の割合に基づいて皮膚疾患の重症度を判定する方法
CA3164066A CA3164066A1 (en) 2019-12-09 2020-12-08 Method for determining severity of skin disease based on percentage of body surface area covered by lesions

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201962945642P 2019-12-09 2019-12-09
US62/945,642 2019-12-09

Publications (1)

Publication Number Publication Date
WO2021116909A1 true WO2021116909A1 (en) 2021-06-17

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PCT/IB2020/061648 Ceased WO2021116909A1 (en) 2019-12-09 2020-12-08 Method for determining severity of skin disease based on percentage of body surface area covered by lesions

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US (2) US11538167B2 (https=)
EP (1) EP4073752A4 (https=)
JP (1) JP7695243B2 (https=)
KR (1) KR20220108158A (https=)
CN (1) CN114830173A (https=)
AU (1) AU2020401794A1 (https=)
BR (1) BR112022011111A2 (https=)
CA (1) CA3164066A1 (https=)
IL (1) IL293654A (https=)
WO (1) WO2021116909A1 (https=)

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US11538167B2 (en) * 2019-12-09 2022-12-27 Janssen Biotech, Inc. Method for determining severity of skin disease based on percentage of body surface area covered by lesions
US11659998B2 (en) 2020-03-05 2023-05-30 International Business Machines Corporation Automatic measurement using structured lights
US11484245B2 (en) * 2020-03-05 2022-11-01 International Business Machines Corporation Automatic association between physical and visual skin properties
CN119206306A (zh) * 2022-03-02 2024-12-27 深圳硅基智能科技有限公司 识别医学图像中的目标的方法及电子设备
CN114882018B (zh) * 2022-06-30 2022-10-25 杭州咏柳科技有限公司 一种基于图像的银屑病严重程度的评估系统
CN115830377B (zh) * 2022-11-28 2026-01-06 青岛大学 基于有限数据的深度学习用于皮肤癌图像分类的方法
US12119118B2 (en) * 2022-12-09 2024-10-15 BelleTorus Corporation Compute system with hidradenitis suppurativa severity diagnostic mechanism and method of operation thereof
WO2025078613A1 (fr) * 2023-10-11 2025-04-17 Term Helvet Sa Procédé de traitement de données relatives à des images

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Also Published As

Publication number Publication date
US11915428B2 (en) 2024-02-27
EP4073752A1 (en) 2022-10-19
US20230060162A1 (en) 2023-03-02
JP2023504901A (ja) 2023-02-07
CN114830173A (zh) 2022-07-29
BR112022011111A2 (pt) 2022-08-23
US11538167B2 (en) 2022-12-27
EP4073752A4 (en) 2024-01-03
JP7695243B2 (ja) 2025-06-18
KR20220108158A (ko) 2022-08-02
CA3164066A1 (en) 2021-06-17
AU2020401794A1 (en) 2022-07-28
US20210174512A1 (en) 2021-06-10
IL293654A (en) 2022-08-01

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