Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

19-09-2020 | Original Article

Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning

Authors: Enric Perera-Bel, Mario Ceresa, Jordina Torrents-Barrena, Narcís Masoller, Brenda Valenzuela-Alcaraz, Eduard Gratacós, Elisenda Eixarch, Miguel A. González Ballester

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 11/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Purpose

Twin-to-twin transfusion syndrome (TTTS) is a serious condition that occurs in about 10–15% of monochorionic twin pregnancies. In most instances, the blood flow is unevenly distributed throughout the placenta anastomoses leading to the death of both fetuses if no surgical procedure is performed. Fetoscopic laser coagulation is the optimal therapy to considerably improve co-twin prognosis by clogging the abnormal anastomoses. Notwithstanding progress in recent years, TTTS surgery is highly risky. Computer-assisted planning of the intervention can thus improve the outcome.

Methods

In this work, we implement a GPU-accelerated random walker (RW) algorithm to detect the placenta, both umbilical cord insertions and the placental vasculature from Doppler ultrasound (US). Placenta and background seeds are manually initialized in 10–20 slices (out of 245). Vessels are automatically initialized in the same slices by means of Otsu thresholding. The RW finds the boundaries of the placenta and reconstructs the vasculature.

Results

We evaluate our semiautomatic method in 5 monochorionic and 24 singleton pregnancies. Although satisfactory performance is achieved on placenta segmentation (Dice ≥ 84.0%), some vascular connections are still neglected due to the presence of US reverberation artifacts (Dice ≥ 56.9%). We also compared inter-user variability and obtained Dice coefficients of ≥ 76.8% and ≥ 97.42% for placenta and vasculature, respectively. After a 3-min manual initialization, our GPU approach speeds the computation 10.6 times compared to the CPU.

Conclusions

Our semiautomatic method provides a near real-time user experience and requires short training without compromising the segmentation accuracy. A powerful approach is thus presented to rapidly plan the fetoscope insertion point ahead of TTTS surgery.

previous article Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques

next article Catheter navigation support for liver radioembolization guidance: feasibility of structure-driven intensity-based registration

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

The Insight Segmentation & Registration Toolkit (ITK): https://itk.org.

Eigen3: http://eigen.tuxfamily.org.

CUDA: https://developer.nvidia.com/cuda-toolkit.

The Medical Imaging Interaction Toolkit (MITK): http://mitk.org.

Benirschke K (1995) The biology of the twinning process: how placentation influences outcome. Semin Perinatol 19:342–350CrossRefPubMed

Denbow ML, Cox P, Taylor M, Hammal DM, Fisk NM (2000) Placental angioarchitecture in monochorionic twin pregnancies: relationship to fetal growth, fetofetal transfusion syndrome, and pregnancy outcome. Am J Obstet Gynecol 182:417–426CrossRefPubMed

Haverkamp F, Lex C, Hanisch C, Fahnenstich H, Zerres K (2001) Neurodevelopmental risks in twin-to-twin transfusion syndrome: preliminary findings. Eur J Paediatr Neourol 5:21–27CrossRef

Rossi AC, D’Addario V (2008) Laser therapy and serial amnioreduction as treatment for twin-twin transfusion syndrome: a metaanalysis and review of literature. Am J Obstet Gynecol 198:147–152CrossRefPubMed

Senat MV, Jan Deprest, Boulvain M, Paupe A, Winer N, Ville Y (2004) Endoscopic laser surgery versus serial amnioreduction for severe twin-to-twin transfusion syndrome. N Engl J Med 351:136–144CrossRefPubMed

Torrents-Barrena J, Piella G, Masoller N, Gratacós E, Eixarch E, Ceresa M, González Ballester MA (2019) Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med Image Anal 51:61–88CrossRefPubMed

Collins SL, Stevenson GN, Noble JA, Impey L (2013) Rapid calculation of standardized placental volume at 11 to 13 weeks and the prediction of small for gestational age babies. Ultrasound Med Biol 36:253–260CrossRef

Cheong KB, Leung KY, Li TKT, Chan HY, Lee YP, Tang MHY (2010) Comparison of inter- and intraobserver agreement and reliability between three different types of placental volume measurement technique (XI VOCAL™, VOCAL™ and multiplanar) and validity in the in-vitro setting. Ultrasound Obstet Gynecol 36:210–217CrossRefPubMed

Stevenson GN, Collins SL, Ding J, Impey L, Noble JA (2015) 3-D ultrasound segmentation of the placenta using the random walker algorithm: reliability and agreement. Ultrasound Med Biol 41:3182–3193CrossRefPubMed

10.

Looney P, Stevenson GN, Nicolaides KH, Plasencia W, Molloholli M, Natsis S (2017) Collins SL (2017) Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning. ISBI 2017:279–282

11.

Yang X, Yu L, Li S, Wen H, Luo D, Bian C, Qin J, Ni D, Heng PA (2019) Towards automated semantic segmentation in prenatal volumetric ultrasound. Trans Med Imaging 38:180–193CrossRef

12.

Oguz BU, Wang J, Yushkevich N, Pouch A, Gee J, Yushkevich PA, Schwartz N, Oguz I (2018) Combining deep learning and multi-atlas label fusion for automated placenta segmentation from 3DUS. In: International workshop on preterm, perinatal and paediatric image analysis. Springer, Cham, pp 138–148

13.

Alansary A, Kamnitsas K, Davidson A, Khlebnikov R, Rajchl M, Malamateniou C, Rutherford M, Hajnal JV, Glocker B, Rueckert D, Kainz B (2016) Fast fully automatic segmentation of the human placenta from motion corrupted MRI. In: International conference on medical image computing and computer-assisted intervention, pp 589–597

14.

Wang G, Zuluaga MA, Pratt R, Aertsen M, Doel T, Klusmann M, David AL, Deprest J, Vercauteren T, Ourselin S (2016) Slic-Seg: a minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views. Med Image Anal 34:137–147CrossRefPubMedPubMedCentral

15.

Wang G, Zuluaga MA, Li W, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) DeepIGeoS: a deep interactive geodesic framework for med. image segmentation. Trans Pattern Anal Mach Intell 41:1559–1572CrossRef

16.

Torrents-Barrena J, Piella G, Masoller N, Gratacós E, Eixarch E, Ceresa M, González Ballester MA (2019) Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI. Med Image Anal 54:263–279CrossRefPubMed

17.

Moccia S, De Momi E, El Hadji S, Mattos LS (2018) Blood vessel segmentation algorithms—review of methods, datasets and evaluation metrics. Comput Methods Programs Biomed 158:71–91CrossRefPubMed

18.

Selle D, Preim B, Schenk A, Peitgen HO (2002) Analysis of vasculature for liver surgical planning. Trans. on Med. Imaging 21:1344–1357CrossRef

19.

Lorigo LM, Faugeras OD, Grimson WEL, Keriven R, Kikinis R, Nabavi A, Westin CF (2001) CURVES: curve evolution for vessel segmentation. Med Image Anal 5:195–200CrossRefPubMed

20.

Rennie MY, Detmar J, Whiteley KJ, Yang J, Jurisicova A, Adamson SL, Sled JG (2011) Vessel tortuousity and reduced vascularization in the fetoplacental arterial tree after maternal exposure to polycyclic aromatic hydrocarbons. Am J Physiol Heart Circ Physiol 300:675–684CrossRef

21.

Cahill LS, Rennie MY, Hoggarth J, Yu LX, Rahman A, Kingdom JC, Seed M, Macgowan CK, Sled JG (2018) Feto- and utero-placental vascular adaptations to chronic maternal hypoxia in the mouse. J Physiol 596:3285–3297CrossRefPubMed

22.

Cheng Y, Hu X, Wang J, Wang Y, Tamura S (2015) Accurate vessel segmentation with constrained B-snake. Trans Image Process 24(8):2440–2455CrossRef

23.

Esneault S, Lafon C, Dillenseger JL (2010) Liver vessels segmentation using a hybrid geometrical moments/graph cuts method. Trans BioMed Eng 57:276–283CrossRef

24.

Meijs M, Manniesing R (2018) Artery and vein segmentation of the cerebral vasculature in 4D CT using a 3D fully convolutional neural network. Med. Imaging 2018: Computer-Aided Diagn., SPIE

25.

Zeng YZ, Zhao YQ, Liao M, Zou BJ, Wang XF, Wang W (2016) Liver vessel segmentation based on extreme learning machine. Phys Medica 32:709–716CrossRef

26.

Anghel C, Archer K, Chang JM, Cochran A, Radulescu A, Salafia CM, Turner R, Djima KY, Zhong L (2018) Placental vessel extraction with Shearlets, Laplacian Eigenmaps, and a conditional generative adversarial network. In: Understanding complex biological systems with mathematics. Springer, Cham, pp 171–196

27.

López-Linares K, Aranjuelo N, Kabongo L, Maclair G, Lete N, Ceresa M, García-Familiar A, Macía I, González Ballester MA (2018) Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks. Med Image Anal 46:202–214CrossRefPubMed

28.

Guo P, Wang Q, Wang X, Hao Z, Xu K, Ren H, Kim JB, Hwang Y (2014) Robust vessel detection and segmentation in ultrasound images by a data-driven approach. SPIE Med Imaging 9034:903435

29.

Schneider C, Guerrero J, Nguan C, Rohling R, Salcudean S (2011) Intra-operative “Pick-Up” ultrasound for robot assisted surgery with vessel extraction and registration: a feasibility study. IPCAI 2011:122–132

30.

Torrents-Barrena J, López-Velazco R, Piella G, Masoller N, Valenzuela-Alcaraz B, Gratacós E, Eixarch E, Ceresa M, González Ballester MA (2019) TTTS-GPS: patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery. Comput Methods Programs Biomed 179:104993CrossRefPubMed

31.

Torrents-Barrena J, Piella G, Masoller N, Gratacos E, Eixarch E, Ceresa M, Gonzalez Ballester MA (2019) Automatic segmentation of the placenta and its peripheral vasculature in volumetric ultrasound for TTTS fetal surgery. In: 2019 IEEE 16th international symposium on biomedical imaging. (ISBI 2019), pp 772–775

32.

Grady L (2006) Random Walks for Image Segmentation. Trans. on Pattern Anal. and Mach. Intell. 28:1768–1783

33.

Jianbing S, Yunfan D, Wenguan W, Xuelong L (2014) Lazy random walks for superpixel segmentation. Trans Med Image Process 23:1451–1462CrossRef

34.

Pujadas ER, Kjer HM, Piella G, González Ballester MA (2016) Iterated random walks with shape prior. Image Vis Comput 54:12–21CrossRef

35.

McCormick M, Liu X, Jomier J, Marion C, Ibanez L (2014) ITK: enabling reproducible research and open science. Front Neuroinform 8:13CrossRefPubMedPubMedCentral

36.

Guennebaud G, Jacob B, Bossart R, Gomez Ferrero D, Nuentsa D et al (2010) Eigen 3. http://eigen.tuxfamily.org

37.

van der Vorst HA (1992) Bi-CGSTAB: A fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems. SIAM J Sci Stat Comput 13(2):631–644CrossRef

38.

Gocławski J, Weffgliński T, Fabijańska A (2015) Accelerating the 3D random walker image segmentation algorithm by image graph reduction and GPU computing. In: Image processing communications challenges 6. Springer, Cham, pp 45–52

Title: Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning
Authors: Enric Perera-Bel
Mario Ceresa
Jordina Torrents-Barrena
Narcís Masoller
Brenda Valenzuela-Alcaraz
Eduard Gratacós
Elisenda Eixarch
Miguel A. González Ballester
Publication date: 19-09-2020
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 11/2020
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-020-02256-2

Springer Professional

Abstract

Purpose

Methods

Results

Conclusions

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 11/2020

A simple tool to automate the insertion process in cochlear implant surgery

IJCARS: MICCAI 2019 special issue

Clearness of operating field: a surrogate for surgical skills on in vivo clinical data

Digitizing rhinoplasty: a web application with three-dimensional preoperative evaluation to assist rhinoplasty surgeons with surgical planning

Fast and accurate online calibration of optical see-through head-mounted display for AR-based surgical navigation using Microsoft HoloLens

Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques

Premium Partner