nach oben

International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

10.02.2017 | Original Article

Validation of a method for retroperitoneal tumor segmentation

verfasst von: Cristina Suárez-Mejías, José A. Pérez-Carrasco, Carmen Serrano, José L. López-Guerra, Tomás Gómez-Cía, Carlos L. Parra-Calderón, Begoña Acha

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 12/2017

Einloggen, um Zugang zu erhalten

Abstract

Purpose

In 2005, an application for surgical planning called AYRA\({\textregistered }\) was designed and validated by different surgeons and engineers at the Virgen del Rocío University Hospital, Seville (Spain). However, the segmentation methods included in AYRA and in other surgical planning applications are not able to segment accurately tumors that appear in soft tissue. The aims of this paper are to offer an exhaustive validation of an accurate semiautomatic segmentation tool to delimitate retroperitoneal tumors from CT images and to aid physicians in planning both radiotherapy doses and surgery.

Methods

A panel of 6 experts manually segmented 11 cases of tumors, and the segmentation results were compared exhaustively with: the results provided by a surgical planning tool (AYRA), the segmentations obtained using a radiotherapy treatment planning system (Pinnacle\(^{\textregistered }\)), the segmentation results obtained by a group of experts in the delimitation of retroperitoneal tumors and the segmentation results using the algorithm under validation.

Results

11 cases of retroperitoneal tumors were tested. The proposed algorithm provided accurate results regarding the segmentation of the tumor. Moreover, the algorithm requires minimal computational time—an average of 90.5% less than that required when manually contouring the same tumor.

Conclusion

A method developed for the semiautomatic selection of retroperitoneal tumor has been validated in depth. AYRA, as well as other surgical and radiotherapy planning tools, could be greatly improved by including this algorithm.

AccuLite, AMIDE, Dicom2, Dicom3Tools, Offis, etc. https://www.xrayscan.com/software-free-dicom-viewers/. Accessed 11 Mar 2016

IDICON et http://www.inf.u-szeged.hu/~idicon/. Accessed 11 Mar 2016

Rubo Medical software et http://www.rubomedical.com/. Accessed 11 Mar 2016

Clijmans T, Gelaude F, Mommaerts M, Suetens P and Vander Sloten J (2006) Computer supported pre-operative planning of craniosynostosis surgery: a MIMICS-integrated approach. In: CMBBE2006, pp. 38–42, Antibes, France

EuHeart Project. http://www.euHeart.eu. Accessed 11 Mar 2016

PASSPORT Project. http://www.vph-institute.org/news/digital-agenda-new-virtual-liver-technology-helps-detect-liver-tumours.html. Accessed 11 Mar 2015

Proplan CMF software. http://cranio-maxillofacial.materialise.com/. Accessed 11 Mar 2016

MIMICS. http://biomedical.materialise.com/mimics. Accessed 11 Mar 2016

AYRA. http://www.ikiria.es/ayra_descripcion_eng.html. Accessed 11 Mar 2016

10.

Suárez C, Acha B, Serrano C, Parra C, Gómez T (2009) VirSSPA—a virtual reality tool for surgical planning workflow. Int J Comput Assist Radiol Surg 4(2):133–139. doi:10.1007/s11548-009-0284-3 CrossRefPubMed

11.

Gacto P, Barrera F, Sicilia-Castro D, Miralles F, Collell M, Leal S, De La Higuera J, Parra C, Gómez-Cía T (2008) A three-dimensional virtual reality model for limb reconstruction in burned patients. BURNS J Int Soc Burn Inj 35(7):1042–1046. doi:10.1016/j.burns.2008.09.005 CrossRef

12.

Gómez-Cía T, Gacto-Sánchez P, Sicilia D, Suárez C, Acha B, Serrano C, Parra C, De La Higuera J (2009) The virtual reality tool VirSSPA in planning DIEP microsurgical breast reconstruction. Int J Comput Assist Radiol Surg 4(4):375–382. doi:10.1007/s11548-009-0311-4 CrossRefPubMed

13.

Gacto-Sánchez P, Sicilia-Castro D, Gómez-Cía T, Lagares A, Collell T, Suárez-Mejías C, Parra C, Leal S, Infante-Cossio De, la Higuera JM (2010) Computerised tomography angiography with VirSSPA 3D software for perforator navigation improves perioperative outcomes in DIEP flap breast reconstruction. Plast Reconstr Surg 125:24–31CrossRefPubMed

14.

Gonzalez RC, Woods RE (2008) Digital image processing. Pearson Prentice Hall, Upper Saddle River

15.

Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2(2000):315–337CrossRefPubMed

16.

Heidari S, Abdullah MT, Abdullah LN (2015) A novel four-directional thresholding approach for lung computed-tomography images by using similarity-based segmentation technique. J Comput Sci 11(1):195–203. doi:10.3844/jcssp.2015.195.203 CrossRef

17.

Dehmeshki J, Amin H, Valdivieso M, Ye X (2008) Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans Med Imaging 27(4):467–480. doi:10.1109/TMI.2007.907555 CrossRefPubMed

18.

Wang XP, Zhang W, Cui Y (2015) Tumor segmentation in lung CT images based on support vector machine and improved level set. Optoelectron Lett 11(5):395–400. doi:10.1007/s11801-015-5148-1 CrossRef

19.

Foruzan AH, Chen YW, Zoroofi RA, Furukawa A, Sato Y, Hori M, Tomiyama N (2013) Segmentation of liver in low-contrast images using K-means clustering and geodesic active contour algorithms. IEICE Trans Inf Syst 96(4):798–807. doi:10.1587/transinf.E96.D.798 CrossRef

20.

Ashraf AB, Gavenonis SC, Daye D, Mies C, Rosen MA, Kontos DA (2013) Multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk. IEEE Trans Med Imaging 32(4):637–648. doi:10.1109/TMI.2012.2219589 CrossRefPubMed

21.

Jiang J, Trundle P, Ren J (2010) Medical image analysis with artificial neural networks. Comput Med Imaging Graph 34(8):617–631. doi:10.1016/j.compmedimag.2010.07.003 CrossRefPubMed

22.

Sethi G, Saini BS (2015) Segmentation of abdomen diseases using active contour models in CT images. Biomed Eng Appl Basis Commun. doi:10.4015/S1016237215500477

23.

Ghose S, Oliver A, Martí R, Lladó X, Vilanova JC, Freixenet J, Mitra J, Sidibé D, Meriaudeau F (2012) A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput Methods Programs Biomed 108(1):262–287. doi:10.1016/j.cmpb.2012.04.006 CrossRefPubMed

24.

Qiu W, Yuan J, Kishimoto J, McLeod J, Chen Y, de Ribaupierre S, Fenster A (2015) User-guided segmentation of preterm neonate ventricular system from 3-D ultrasound images using convex optimization. Ultrasound Med Biol 41(2):542–556. doi:10.1016/j.ultrasmedbio.2014.09.019 CrossRefPubMed

25.

Peng J, Wang Y, Kong D (2013) Liver segmentation with constrained convex variational model. Pattern Recogn Lett 43(1):81–88. doi:10.1016/j.patrec.2013.07.010

26.

Zhou Y, Shi WR, Chen W, Chen YL, Li Y, Tan LW, Chen DQ (2015) Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images. Neurocomputing 156:199–210. doi:10.1016/j.neucom.2014.12.061 CrossRef

27.

Ju W, Xiang D, Zhang B, Wang L, Kopriva I, Chen X (2015) Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Trans Image Process 24(12):5854–5867. doi:10.1109/TIP.2015.2488902 CrossRefPubMed

28.

Casciaro S, Franchini R, Massoptier L, Casciaro E, Conversano F, Malvasi A, Lay-Ekuakille A (2012) Fully automatic segmentations of liver and hepatic tumors from 3-D computed tomography abdominal images: comparative evaluation of two automatic methods. IEEE Sens J 12(3):464–473. doi:10.1109/JSEN.2011.2108281 CrossRef

29.

Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563. doi:10.1016/j.media.2009.05.004 CrossRefPubMed

30.

Chen DR, Chang RF, Wu WJ, Moon WK, Wu WL (2003) 3-D breast ultrasound segmentation using active contour model. Ultrasound Med Biol 29(7):1017–1026. doi:10.1016/S0301-5629(03)00059-0 CrossRefPubMed

31.

Droske M, Meyer B, Rumpf M, Schaller C (2005) An adaptive level set method for interactive segmentation of intracranial tumors. Neurol Res 27(4):363–370. doi:10.1179/016164105X48842 CrossRefPubMed

32.

Qiu W, Yuan J, Ukwatta E, Sun Y, Rajchl M, Fenster A (2014) Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-D tRUS and MR images. IEEE Trans Med Imaging 33(4):947–960. doi:10.1109/TMI.2014.2300694 CrossRefPubMed

33.

Yuan J, Bae E, Tai XC (2010) A study on continuous max-flow and min-cut approaches. In: Proceedings of the IEEE Computer Society conference on computer vision and pattern recognition, pp 2217–2224. doi:10.1109/CVPR.2010.5539903

34.

Yuan J, Ukwatta E, Tai XC, Fenster A, Schnoerr C (2012) A fast global optimization-based approach to evolving contours with generic shape prior. UCLA technical report CAM: 12-38

35.

Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):3243–3254. doi:10.1109/TIP.2010.2069690 CrossRefPubMed

36.

Boykov Y, Funka-Lea G (2006) Graph cuts and efficient N-D image segmentation. Int J Comput Vis 70(2):109–131. doi:10.1007/s11263-006-7934-5 CrossRef

37.

Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277. doi:10.1109/83.902291 CrossRefPubMed

38.

Yousefi H, Fatehi M, Amian M, Zoroofi RA (2013) A fully automated segmentation of radius bone based on active contour in wrist MRI data set. In: 2013 20th Iranian conference on biomedical engineering, ICBME 2013. doi:10.1109/ICBME.2013.6782190

39.

Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24(1):205–219. doi:10.1016/j.media.2015.06.012 CrossRefPubMedPubMedCentral

40.

Li BN, Chui CK, Chang S, Ong SH (2012) A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst Appl 39(10):9661–9668. doi:10.1016/j.eswa.2012.02.095 CrossRef

41.

Rajagopal R, Subbaiah P (2015) A survey on liver tumor detection and segmentation methods. ARPN J Eng Appl Sci 10(6):2681–2685

42.

Kalpathy-Cramer J, Zhao B, Goldgof D, Gu Y, Wang X, Yang H, Tan Y, Gillies R, Napel S (2016) A comparison of lung nodule segmentation algorithms: methods and results from a multi-institutional study. J Digit Imaging 29(4):476–487. doi:10.1007/s10278-016-9859-z CrossRefPubMedPubMedCentral

43.

Litjens G, Toth R, van de Ven W, Hoeks C, Kerkstra S, van Ginneken B, Vincent G, Guillard G, Birbeck N, Zhang J, Strand R, Malmberg F, Ou Y, Davatzikos C, Kirschner M, Jung F, Yuan J, Qiu W, Gao Q, Edwards PE, Maan B, van der Heijden F, Ghose S, Mitra J, Dowling J, Barratt D, Huisman H, Madabhushi A (2014) Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge (2014). Med Image Anal 18(2):359–373. doi:10.1016/j.media.2013.12.002 CrossRefPubMed

44.

Tavakoli V, Amini AA (2013) A survey of shaped-based registration and segmentation techniques for cardiac images. Comput Vis Image Underst 117(9):966–989. doi:10.1016/j.cviu.2012.11.017 CrossRef

45.

Punithakumar K, Yuan J, Ben Ayed I, Li S, Boykov Y (2012) A convex max-flow approach to distribution-based figure-ground separation. SIAM J Imaging Sci 5(4):1333–1354. doi:10.1137/110850372 CrossRef

46.

Strauss DC, Hayes AJ, Thomas JM (2011) Retroperitoneal tumours: review of management. Ann R Coll Surg Engl 93(4):275–280. doi:10.1308/003588411X571944 CrossRefPubMedPubMedCentral

47.

Brennan C, Kajal D, Khalili K, Ghai S (2014) Solid malignant retroperitoneal masses—a pictorial review. Insights Imaging 5(1):53–65. doi:10.1007/s13244-013-0294-0 CrossRefPubMed

48.

Rajiah P, Sinha R, Cuevas C, Dubinsky TJ, Bush WH, Kolokythas O (2011) Imaging of uncommon retroperitoneal masses. RadioGraphics 31(4):949–976. doi:10.1148/rg.314095132 CrossRefPubMed

49.

Monsky WL, Jin B, Molloy C, Canter RJ, Li CS, Lin TC, Borys D, Mack W, Kim I, Buonocore MH, Chaudhari AJ (2012) Semi-automated volumetric quantification of tumor necrosis in soft tissue sarcoma using contrast-enhanced MRI. Anticancer Res 32(11):4951–4962PubMedPubMedCentral

50.

Suárez-Mejías C, Pérez-Carrasco JA, Serrano C, López-Guerra JL, Parra-Calderón C, Gómez-Cía T, Acha B (2016) Three dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning. Med Biol Eng Comput (MBEC). doi:10.1007/s11517-016-1505-x

51.

Pérez-Carrasco JA, Suárez-Mejías C, Serrano C, López-Guerra JL, Acha B (2014) Segmentation of retroperitoneal tumors using fast continuous max-flow algorithm. IFMBE Proc 41:360–363. doi:10.1007/978-3-319-00846-2_89 CrossRef

52.

Yuan J, Bae E, Xue-Cheng T, Yuri B (2010) A continuous max-flow approach to Potts model. In: ECCV 2010, Part VI, LNCS 6316, pp 379–392

53.

Vincent L (1998) Minimal path algorithms for the robust detection of linear features in gray images. In: ISSM, pp 331–338

54.

Mendoza C, Acha Piñero B, Serrano Gotarredona MC, Gómez Cía PT (2009) Self-assessed contrast-maximizing adaptive region growing. Lecture Notes in Computer Science, vol 580, pp 652–663

55.

Mendoza C, Acha Piñero B, Serrano Gotarredona MC, Gómez Cía PT (2012) Fast parameter-free region growing segmentation with application to surgical planning. Mach Vis Appl 23(1):165–177CrossRef

56.

Pinnacle 9.8 at http://www.healthcare.philips.com/main/products/ros/products/pinnacle3_98/. Accessed 11 Mar 2016

57.

Jaccard P (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaud Sci Nat 37:547–579

58.

DICE coefficient at http://sve.loni.ucla.edu/instructions/metrics/dice/. Accessed 20 Nov 2015

59.

Chang H, Zhuang AH, Valentino DJ, Chi WC (2009) Performance measure characterization for evaluating neuroimage segmentation algorithms. NeuroImage 47:122–135. doi:10.1016/j.neuroimage.2009.03.068 CrossRefPubMed

60.

Estrada J, Jepson A (2005) Quantitative evaluation of a novel image segmentation algorithm. In: IEEE Computer Society conference on computer vision and pattern recognition, CVPR, pp 1132–1139

61.

Xu J, Chutatape O, Chew P (2007) Automated optic disk boundary detection by modified active contour model. IEEE Trans Biomed Eng 54(3):473–482. doi:10.1109/TBME.2006.888831.63 CrossRefPubMed

Titel: Validation of a method for retroperitoneal tumor segmentation
verfasst von: Cristina Suárez-Mejías
José A. Pérez-Carrasco
Carmen Serrano
José L. López-Guerra
Tomás Gómez-Cía
Carlos L. Parra-Calderón
Begoña Acha
Publikationsdatum: 10.02.2017
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 12/2017
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1530-8

Neu im Fachgebiet Radiologie

23.04.2024 | Pankreaskarzinom | Nachrichten

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Validation of a method for retroperitoneal tumor segmentation

Abstract

Purpose

Methods

Results

Conclusion

Neu im Fachgebiet Radiologie

S3-Leitlinie zu Pankreaskrebs aktualisiert

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Update Radiologie

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 12/2017

Initial experience using a robotic-driven laparoscopic needle holder with ergonomic handle: assessment of surgeons’ task performance and ergonomics

Multimodal image registration based on binary gradient angle descriptor

Error range in proximal femoral osteotomy using computer tomography-based navigation

Validation of three-dimensional models of the distal femur created from surgical navigation point cloud data for intraoperative and postoperative analysis of total knee arthroplasty

The computer-aided parallel external fixator for complex lower limb deformity correction

PCA-based localization approach for segmentation of optic disc

Neu im Fachgebiet Radiologie

S3-Leitlinie zu Pankreaskrebs aktualisiert

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Update Radiologie