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Extract from the Register of European Patents

EP About this file: EP3864620

EP3864620 - CORRECTING SEGMENTATION OF MEDICAL IMAGES USING A STATISTICAL ANALYSIS OF HISTORIC CORRECTIONS [Right-click to bookmark this link]
StatusNo opposition filed within time limit
Status updated on  24.02.2023
Database last updated on 02.11.2024
FormerThe patent has been granted
Status updated on  18.03.2022
FormerGrant of patent is intended
Status updated on  12.01.2022
FormerRequest for examination was made
Status updated on  01.12.2021
FormerGrant of patent is intended
Status updated on  15.11.2021
FormerRequest for examination was made
Status updated on  16.07.2021
FormerThe international publication has been made
Status updated on  25.06.2021
Formerunknown
Status updated on  08.01.2020
Most recent event   Tooltip27.09.2024Lapse of the patent in a contracting state
New state(s): MT		published on 30.10.2024  [2024/44]
Applicant(s)For all designated states
Brainlab AG
Olof-Palme-Str. 9
81829 München / DE
[2022/16]
Former [2021/33]For all designated states
Brainlab AG
Olof-Palme-Str. 9
81829 Munich / DE
Inventor(s)01 / Vilsmeier, Stefan
c/o Brainlab AG
Olof-Palme-Strasse 9
81829 Munich / DE
02 / Blumenhofer, Andreas
c/o Brainlab AG
Olof-Palme-Strasse 9
81829 Munich / DE
03 / Schmaler, Jens
c/o Brainlab AG
Olof-Palme-Strasse 9
81829 Munich / DE
 [2022/16]
Representative(s)SSM Sandmair
Patentanwälte Rechtsanwalt
Partnerschaft mbB
Joseph-Wild-Straße 20
81829 München / DE
[2021/33]
Application number, filing date19828774.020.12.2019
[2021/33]
WO2019EP86789
Filing languageEN
Procedural languageEN
PublicationType: A1 Application with search report
No.:WO2021121631
Date:24.06.2021
Language:EN
[2021/25]
Type: A1 Application with search report 
No.:EP3864620
Date:18.08.2021
Language:EN
The application published by WIPO in one of the EPO official languages on 24.06.2021 takes the place of the publication of the European patent application.
[2021/33]
Type: B1 Patent specification 
No.:EP3864620
Date:20.04.2022
Language:EN
[2022/16]
Search report(s)International search report - published on:EP24.06.2021
ClassificationIPC:G06T7/174, G06T7/143, G06T7/00
[2021/33]
CPC:
G06T7/174 (EP,US); G06T7/0012 (EP,CN,US); G06T7/143 (EP,US);
G06N20/20 (CN); G06N3/045 (CN); G06N3/08 (CN);
G06T7/11 (CN); G06T2207/10088 (CN); G06T2207/10116 (CN);
G06T2207/10132 (CN); G06T2207/20081 (EP,CN,US); G06T2207/20084 (EP,CN,US);
G06T2207/20092 (EP,US); G06T2207/20128 (EP,US); G06T2207/30004 (US) (-)
Designated contracting statesAL,   AT,   BE,   BG,   CH,   CY,   CZ,   DE,   DK,   EE,   ES,   FI,   FR,   GB,   GR,   HR,   HU,   IE,   IS,   IT,   LI,   LT,   LU,   LV,   MC,   MK,   MT,   NL,   NO,   PL,   PT,   RO,   RS,   SE,   SI,   SK,   SM,   TR [2021/33]
Extension statesBANot yet paid
MENot yet paid
Validation statesKHNot yet paid
MANot yet paid
MDNot yet paid
TNNot yet paid
TitleGerman:KORREKTUR DER SEGMENTIERUNG MEDIZINISCHER BILDER UNTER VERWENDUNG EINER STATISTISCHEN ANALYSE HISTORISCHER KORREKTUREN[2021/33]
English:CORRECTING SEGMENTATION OF MEDICAL IMAGES USING A STATISTICAL ANALYSIS OF HISTORIC CORRECTIONS[2021/33]
French:CORRECTION DE SEGMENTATION D'IMAGES MÉDICALES À L'AIDE D'UNE ANALYSE STATISTIQUE DE CORRECTIONS HISTORIQUES[2021/33]
Entry into regional phase05.02.2021National basic fee paid 
05.02.2021Designation fee(s) paid 
05.02.2021Examination fee paid 
Examination procedure05.02.2021Amendment by applicant (claims and/or description)
05.02.2021Examination requested  [2021/33]
05.02.2021Date on which the examining division has become responsible
16.11.2021Communication of intention to grant the patent
01.12.2021Disapproval of the communication of intention to grant the patent by the applicant or resumption of examination proceedings by the EPO
13.01.2022Communication of intention to grant the patent
15.03.2022Fee for grant paid
15.03.2022Fee for publishing/printing paid
15.03.2022Receipt of the translation of the claim(s)
Opposition(s)23.01.2023No opposition filed within time limit [2023/13]
Fees paidRenewal fee
24.12.2021Renewal fee patent year 03
Opt-out from the exclusive  Tooltip
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Patent Court		 See the Register of the Unified Patent Court for opt-out data
Responsibility for the accuracy, completeness or quality of the data displayed under the link provided lies entirely with the Unified Patent Court.
Lapses during opposition  TooltipHU20.12.2019
AL20.04.2022
AT20.04.2022
CY20.04.2022
CZ20.04.2022
DK20.04.2022
EE20.04.2022
ES20.04.2022
FI20.04.2022
HR20.04.2022
IT20.04.2022
LT20.04.2022
LV20.04.2022
MC20.04.2022
MK20.04.2022
MT20.04.2022
NL20.04.2022
PL20.04.2022
RO20.04.2022
RS20.04.2022
SE20.04.2022
SK20.04.2022
SM20.04.2022
TR20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
LU20.12.2022
BE31.12.2022
CH31.12.2022
FR31.12.2022
LI31.12.2022
[2024/44]
Former [2024/29]HU20.12.2019
AL20.04.2022
AT20.04.2022
CY20.04.2022
CZ20.04.2022
DK20.04.2022
EE20.04.2022
ES20.04.2022
FI20.04.2022
HR20.04.2022
IT20.04.2022
LT20.04.2022
LV20.04.2022
MC20.04.2022
MK20.04.2022
NL20.04.2022
PL20.04.2022
RO20.04.2022
RS20.04.2022
SE20.04.2022
SK20.04.2022
SM20.04.2022
TR20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
LU20.12.2022
BE31.12.2022
CH31.12.2022
FR31.12.2022
LI31.12.2022
Former [2024/27]HU20.12.2019
AL20.04.2022
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CZ20.04.2022
DK20.04.2022
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NL20.04.2022
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SE20.04.2022
SK20.04.2022
SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
LU20.12.2022
BE31.12.2022
CH31.12.2022
FR31.12.2022
LI31.12.2022
Former [2024/25]HU20.12.2019
AL20.04.2022
AT20.04.2022
CY20.04.2022
CZ20.04.2022
DK20.04.2022
EE20.04.2022
ES20.04.2022
FI20.04.2022
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IT20.04.2022
LT20.04.2022
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NL20.04.2022
PL20.04.2022
RO20.04.2022
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SE20.04.2022
SK20.04.2022
SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
LU20.12.2022
BE31.12.2022
CH31.12.2022
FR31.12.2022
LI31.12.2022
Former [2024/21]AL20.04.2022
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CZ20.04.2022
DK20.04.2022
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FI20.04.2022
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PL20.04.2022
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SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
LU20.12.2022
BE31.12.2022
CH31.12.2022
FR31.12.2022
LI31.12.2022
Former [2024/11]AL20.04.2022
AT20.04.2022
CZ20.04.2022
DK20.04.2022
EE20.04.2022
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FI20.04.2022
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LT20.04.2022
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NL20.04.2022
PL20.04.2022
RO20.04.2022
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SE20.04.2022
SK20.04.2022
SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
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CH31.12.2022
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Former [2023/52]AL20.04.2022
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SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
LU20.12.2022
BE31.12.2022
CH31.12.2022
FR31.12.2022
LI31.12.2022
Former [2023/49]AL20.04.2022
AT20.04.2022
CZ20.04.2022
DK20.04.2022
EE20.04.2022
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FI20.04.2022
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LT20.04.2022
LV20.04.2022
NL20.04.2022
PL20.04.2022
RO20.04.2022
RS20.04.2022
SE20.04.2022
SK20.04.2022
SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
LU20.12.2022
BE31.12.2022
CH31.12.2022
LI31.12.2022
Former [2023/48]AL20.04.2022
AT20.04.2022
CZ20.04.2022
DK20.04.2022
EE20.04.2022
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FI20.04.2022
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LT20.04.2022
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NL20.04.2022
PL20.04.2022
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SE20.04.2022
SK20.04.2022
SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
IE20.12.2022
LU20.12.2022
CH31.12.2022
LI31.12.2022
Former [2023/47]AL20.04.2022
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SM20.04.2022
BG20.07.2022
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GR21.07.2022
IS20.08.2022
PT22.08.2022
LU20.12.2022
CH31.12.2022
LI31.12.2022
Former [2023/38]AL20.04.2022
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EE20.04.2022
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SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
LU20.12.2022
Former [2023/17]AL20.04.2022
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RO20.04.2022
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SE20.04.2022
SK20.04.2022
SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
Former [2023/09]AT20.04.2022
CZ20.04.2022
DK20.04.2022
EE20.04.2022
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FI20.04.2022
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LT20.04.2022
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NL20.04.2022
PL20.04.2022
RO20.04.2022
RS20.04.2022
SE20.04.2022
SK20.04.2022
SM20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
Former [2023/07]AT20.04.2022
DK20.04.2022
ES20.04.2022
FI20.04.2022
HR20.04.2022
LT20.04.2022
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NL20.04.2022
PL20.04.2022
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SE20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
Former [2022/51]AT20.04.2022
ES20.04.2022
FI20.04.2022
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LT20.04.2022
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NL20.04.2022
PL20.04.2022
RS20.04.2022
SE20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
IS20.08.2022
PT22.08.2022
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PL20.04.2022
RS20.04.2022
SE20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
PT22.08.2022
Former [2022/49]AT20.04.2022
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FI20.04.2022
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NL20.04.2022
SE20.04.2022
BG20.07.2022
NO20.07.2022
GR21.07.2022
PT22.08.2022
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NL20.04.2022
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PT22.08.2022
Former [2022/44]NL20.04.2022
Cited inInternational search[XA]  - Kirsten Nicole ET AL, "Evaluation of Automatic and Manual Segmentations in Neonate Subcortical Structures", (20080101), URL: https://pdfs.semanticscholar.org/f08c/5396f25423c851d263c597e739f5f96d5385.pdf, (20200806), XP055720610 [X] 1,4-7,15-17 * abstract * * section 2 * * figures 3-5 * [A] 2,3,8-14
 [A]  - WANG HONGZHI ET AL, Standing on the Shoulders of Giants: Improving Medical Image Segmentation via Bias Correction, ANNUAL INTERNATIONAL CONFERENCE ON THE THEORY AND APPLICATIONS OF CRYPTOGRAPHIC TECHNIQUES, EUROCRYPT 2018; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE(S) 105 - 112, (20100920), ISBN 978-3-642-17318-9, XP047463035 [A] 1-17 * abstract * * sections 2, 3.2 *

DOI:   http://dx.doi.org/10.1007/978-3-642-15711-0_14
by applicant   - JAMIE SHOTTONENDER KONUKOGLU, Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning, (20110000), URL: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/CriminisiForests_FoundTrends_2011.pdf
    - I. GOODFELLOWY. BENGIOA. COURVILLE, Deep learning, chapter convolutional networks, (20160000), URL: http://www.deeplearningbook.org
    - J. WU, "Common loss functions in machine learning", Introduction to convolutional neural networks, URL: https://pdfs.semanticscholar.org/450c/a19932fcef1ca6d0442cbf52fec38fb9d1e5.pdf
    - ALEX KRIZHEVSKYILYA SUTSKEVERGEOFFREY E. HINTON, Imagenet classification with deep convolutional neural networks, URL: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
    - S. RENK. HER. GIRSHICKJ. SUN, Faster r-cnn: Towards real-time object detection with region proposal networks, URL: https://arxiv.org/pdf/1506.01497.pdf
    - S.-E. WEIV. RAMAKRISHNAT. KANADEY. SHEIKH, Convolutional pose machines, URL: https://arxiv.0rg/pdf/1602.00134.pdf
    - JONATHAN LONGEVAN SHELHAMERTREVOR DARRELL, Fully convolutional networks for semantic segmentation, URL: https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf
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