Literature DB >> 26889498

Interactive Whole-Heart Segmentation in Congenital Heart Disease.

Danielle F Pace1, Adrian V Dalca1, Tal Geva2, Andrew J Powell2, Mehdi H Moghari2, Polina Golland1.   

Abstract

We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.

Entities:  

Year:  2015        PMID: 26889498      PMCID: PMC4753059          DOI: 10.1007/978-3-319-24574-4_10

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  7 in total

1.  Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.

Authors:  Pierrick Coupé; José V Manjón; Vladimir Fonov; Jens Pruessner; Montserrat Robles; D Louis Collins
Journal:  Neuroimage       Date:  2010-09-17       Impact factor: 6.556

Review 2.  Challenges and methodologies of fully automatic whole heart segmentation: a review.

Authors:  Xiahai Zhuang
Journal:  J Healthc Eng       Date:  2013       Impact factor: 2.682

3.  Three-dimensional printing in cardiac surgery and interventional cardiology: a single-centre experience.

Authors:  Daniel Schmauss; Sandra Haeberle; Christian Hagl; Ralf Sodian
Journal:  Eur J Cardiothorac Surg       Date:  2014-08-26       Impact factor: 4.191

4.  A supervised patch-based approach for human brain labeling.

Authors:  Françcois Rousseau; Piotr A Habas; Colin Studholme
Journal:  IEEE Trans Med Imaging       Date:  2011-05-19       Impact factor: 10.048

5.  4D ACTIVE CUT: AN INTERACTIVE TOOL FOR PATHOLOGICAL ANATOMY MODELING.

Authors:  Bo Wang; Wei Liu; Marcel Prastawa; Andrei Irimia; Paul M Vespa; John D van Horn; P Thomas Fletcher; Guido Gerig
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2014-04

6.  Three-dimensional patient-specific cardiac model for surgical planning in Nikaidoh procedure.

Authors:  Israel Valverde; Gorka Gomez; Antonio Gonzalez; Cristina Suarez-Mejias; Alejandro Adsuar; Jose Felix Coserria; Sergio Uribe; Tomas Gomez-Cia; Amir Reza Hosseinpour
Journal:  Cardiol Young       Date:  2014-05-09       Impact factor: 1.093

7.  3D-Imaging of cardiac structures using 3D heart models for planning in heart surgery: a preliminary study.

Authors:  Stephan Jacobs; Ronny Grunert; Friedrich W Mohr; Volkmar Falk
Journal:  Interact Cardiovasc Thorac Surg       Date:  2007-10-09
  7 in total
  18 in total

Review 1.  Update on the Role of Cardiac Magnetic Resonance Imaging in Congenital Heart Disease.

Authors:  Prabhakar Rajiah; Animesh Tandon; Gerald F Greil; Suhny Abbara
Journal:  Curr Treat Options Cardiovasc Med       Date:  2017-01

2.  Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease.

Authors:  Danielle F Pace; Adrian V Dalca; Tom Brosch; Tal Geva; Andrew J Powell; Jürgen Weese; Mehdi H Moghari; Polina Golland
Journal:  Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018)       Date:  2018-09-20

Review 3.  Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need.

Authors:  Arghavan Arafati; Peng Hu; J Paul Finn; Carsten Rickers; Andrew L Cheng; Hamid Jafarkhani; Arash Kheradvar
Journal:  Cardiovasc Diagn Ther       Date:  2019-10

4.  An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training.

Authors:  Hao Zheng; Yizhe Zhang; Lin Yang; Chaoli Wang; Danny Z Chen
Journal:  Proc Conf AAAI Artif Intell       Date:  2020-04-03

5.  A Heart Segmentation Algorithm Based on Dynamic Ultrasound.

Authors:  Mingjun Tian; Minjuan Zheng
Journal:  Biomed Res Int       Date:  2022-06-17       Impact factor: 3.246

6.  Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation.

Authors:  Jamshid Sourati; Ali Gholipour; Jennifer G Dy; Sila Kurugol; Simon K Warfield
Journal:  Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018)       Date:  2018-09-20

Review 7.  Applications of artificial intelligence in cardiovascular imaging.

Authors:  Maxime Sermesant; Hervé Delingette; Hubert Cochet; Pierre Jaïs; Nicholas Ayache
Journal:  Nat Rev Cardiol       Date:  2021-03-12       Impact factor: 32.419

8.  A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI.

Authors:  Chao Ma; Gongning Luo; Kuanquan Wang
Journal:  Biomed Res Int       Date:  2017-02-19       Impact factor: 3.411

9.  Unsupervised Medical Image Segmentation Based on the Local Center of Mass.

Authors:  Iman Aganj; Mukesh G Harisinghani; Ralph Weissleder; Bruce Fischl
Journal:  Sci Rep       Date:  2018-08-29       Impact factor: 4.379

10.  Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients.

Authors:  Gerhard-Paul Diller; Aleksander Kempny; Sonya V Babu-Narayan; Marthe Henrichs; Margarita Brida; Anselm Uebing; Astrid E Lammers; Helmut Baumgartner; Wei Li; Stephen J Wort; Konstantinos Dimopoulos; Michael A Gatzoulis
Journal:  Eur Heart J       Date:  2019-04-01       Impact factor: 29.983

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