Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-04-2017 | Original Article

Integrating CAD modules in a PACS environment using a wide computing infrastructure

Authors: Jorge J. Suárez-Cuenca, Amara Tilve, Ricardo López, Gonzalo Ferro, Javier Quiles, Miguel Souto

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2017

Abstract

Purpose

The aim of this paper is to describe a project designed to achieve a total integration of different CAD algorithms into the PACS environment by using a wide computing infrastructure.

Methods

The aim is to build a system for the entire region of Galicia, Spain, to make CAD accessible to multiple hospitals by employing different PACSs and clinical workstations. The new CAD model seeks to connect different devices (CAD systems, acquisition modalities, workstations and PACS) by means of networking based on a platform that will offer different CAD services. This paper describes some aspects related to the health services of the region where the project was developed, CAD algorithms that were either employed or selected for inclusion in the project, and several technical aspects and results.

Results

We have built a standard-based platform with which users can request a CAD service and receive the results in their local PACS. The process runs through a web interface that allows sending data to the different CAD services. A DICOM SR object is received with the results of the algorithms stored inside the original study in the proper folder with the original images.

Conclusions

As a result, a homogeneous service to the different hospitals of the region will be offered. End users will benefit from a homogeneous workflow and a standardised integration model to request and obtain results from CAD systems in any modality, not dependant on commercial integration models. This new solution will foster the deployment of these technologies in the entire region of Galicia.

Doi K (2005) Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 78:S3–S19CrossRefPubMed

Astley SM, Gilbert FJ (2004) Computer-aided detection in mammography. Clin Radiol 59(5):390–399CrossRefPubMed

Armato SG III, Li F, Giger ML, MacMahon H, Sone S, Doi K (2002) Lungcancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology 225:685–692CrossRefPubMed

Li Q (2007) Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Comput Med Imaging Graph 31:248–257CrossRefPubMedPubMedCentral

Sluimer I, Schilham A, Prokop M, van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 25:385–405CrossRefPubMed

Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4–5):198–211CrossRefPubMedPubMedCentral

Souto M, Suárez-Cuenca JJ, Tahoces PG, Carreira JM, Remy-Jardin M, Remy J (2012) Automatic detection of pulmonary nodules: evaluation of performance using two different MDCT scanners. J Biomed Graph Comput 2(2):55–63

Suárez-Cuenca JJ, Tahoces PG, Souto M, Lado MJ, Remy-Jardin M, Remy J, Vidal JJ (2009) Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images. Comput Biol Med 39(10):921–933CrossRefPubMed

Souto M, Tahoces PG, Suárez-Cuenca JJ, Lado MJ, Remy-Jardin M, Remy J, Vidal JJ (2008) Automatic detection of pulmonary nodules on computed tomography: a preliminary study. Radiología 50:387–392CrossRefPubMed

10.

Li Q, Sone S, Doi K (2003) Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys 30:2040–2051CrossRefPubMed

11.

Choi WJ, Choi TS (2014) Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Programs Biomed 113(1):37–54CrossRefPubMed

12.

valli Gogula S, Divakar CH, Satyanarayana CH, Appa Rao A (2014) A diabetic retinopathy detection method using an improved pillar K-means algorithm. Bioinformation 10(1):28–32CrossRef

13.

Abbas Q, García IF, Rashid M (2010) Automatic skin tumour border detection for digital dermoscopy using a new digital image analysis scheme. Br J Biomed Sci 67(4):177–183CrossRefPubMed

14.

Huang HK, Doi K (2007) Editorial and special issue on CAD and image-guided decision support. Comput Med Imaging Graph 31(4–5):195–197

15.

Le AHT, Liu BJ, Huang HK (2009) Integration of computer-aided diagnosis/detection (CAD) results in a PACS Environment using CAD-PACS toolkit and DICOM SR. Int J Comput Assist Radiol Surg 4(4):317–329CrossRefPubMed

16.

Engelmann U, Meinzer HP, Schröter A, Günnel U, Am Demiris M, Makabe H Evers, Jean FC, Degoulet P (1995) The image related services of the HELIOS software engineering environment. Comput Methods Programs Biomed 46(1):1–12CrossRefPubMed

17.

Health Level 7. http://www.hl7.org/

18.

Hussein R, Engelmann U, Schroeter A, Meinzer HP (2004) DICOM structured reporting: part 1. Overview and characteristics. RadioGraphics 24:891–896CrossRefPubMed

19.

Henderson M, Behlen F, Parisot C, Siegel EL, Channin DS (2001) Integrating the healthcare enterprise: a primer. Part 4. The role of existing standards in IHE. RadioGraphics 21(6):1597–1603

20.

Channin DS (2001) Integrating the healthcare enterprise: a primer. Part 2. Seven brides for seven brothers: the IHE integration profiles. RadioGraphics 21(5):1343–1350CrossRefPubMed

21.

IHE. http://www.ihe.net

22.

DICOM Standard. http://dicom.nema.org/standard.html

23.

Área de Epidemiología Ambiental y Cáncer, CentroNacional de Epidemiología, Instituto de Salud Carlos III,(2006) La situación del cáncer en España. Ministerio deSanidad y Consumo, Madrid

24.

Romero C, Almenar A, Pinto JM, Varela C, Muñoz E, Botella M (2011) Impact on breast cancer diagnosis in a multidisciplinary unit after the incorporation of mammography digitalization and computer-aided detection systems. Am J Roentgenol 197:1492–1497CrossRef

25.

Karssemeijer N, Otten JD, Verbeek AL, Groenewoud JH, de Koning HJ, Hendriks JH, Holland R (2003) Computer-aided detection versus independent double reading of masses on mammograms. Radiology 227(1):192–200CrossRefPubMed

26.

Wei J, Hagihara Y, Shimizu A, Kobatake H (2002) Optimal image feature set for detecting lung nodules on chest X-ray images. Proc Int Workshop CARS 2002:706–711

27.

Coppini G, Diciotti S, Falchini M, Villari N, Valli G (2003) Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms. IEEE Trans Inf Technol Biomed 7(4):344–357CrossRefPubMed

Title: Integrating CAD modules in a PACS environment using a wide computing infrastructure
Authors: Jorge J. Suárez-Cuenca
Amara Tilve
Ricardo López
Gonzalo Ferro
Javier Quiles
Miguel Souto
Publication date: 01-04-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 4/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1532-6

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Integrating CAD modules in a PACS environment using a wide computing infrastructure

Abstract

Purpose

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 4/2017

The numerical stability of transformation-based CT ventilation

Automatic gallbladder and gallstone regions segmentation in ultrasound image

A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans

Acquisition models in intraoperative positron surface imaging

Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI

Morphology filter bank for extracting nodular and linear patterns in medical images