Abstract

Introduction and objective: In computer aided diagnosis (CAD) tools searching for colonrectal polyps and based on three dimensions virtual colonoscopy (3DVC) using computed tomography (CT) images, the reduction of the occurrence of false-positives (FPs) still represents a challenge because they are source of unreliability. Following an encouraging previous supervised approach Bevilacqua et al., Three-dimensional Virtual Colonoscopy for Polyps Detection by Supervised Artificial Neural Networks D.-S. Huang et al. (Eds.): ICIC, LNBI 6840, Springer-Verlag, Berlin Heidelberg, (2011), pp. 596-603, the aim of this work is to discuss, in details, how the adopted strategies, designed and tested on an initial reduced data set, reveals good performance and robustness in terms of FPs reduction on an enlarged cohort of new cases. Materials and methods: At the beginning, materials consisted only in 10 different polyps, diagnosed, by expert radiologists, in 6 different patients, scanning 16 rows helical CT multi slices with a resolution of 1. mm. Moreover from those 10 polyps only 7 polyps were initially used for the analysis, excluding 2 tumors with diameter bigger than 1. cm, and one polyp hardly recognizable due to fecal stool. In this paper, thanks to a new accurate phase of collecting data, materials grow impressively and then consist in total of 43 polyps all useful for the study. The whole data set was merged by using the former data set of colonrectal exams from the clinical operative unit called "Sezione di Diagnostica per Immagini" of Di.M.I.M.P. of Policlinico of Bari and the new ones coming from two new collaborations: the Oncology department of Faculty of Medicine of University of Pisa participating, as the former, to the IMPACT study (Italian Multicenter Polyps Accuracy CTC Study) Regge, Linear and nonlinear feedforward neural network classifiers: a comprehensive understanding, J. Intell. Syst., 9 (1) 1999, 1-38 and, more recently, the operative unit of radiology of the "Istituto Tumori Giovanni Paolo II" of Bari. Starting from computed tomography colonography (CTC) images, several volumes were scanned by means of three different supervised artificial neural networks (ANNs) architectures based on error back propagation training algorithm Huang and Ma, Linear and nonlinear feedforward neural network classifiers: a comprehensive understanding, J. Intell. Syst., 9 (1) 1999, 1-38. All the training sets were built by using polyps and non-polyps sub-volume samples, whose dimensions were correlated to the volume of the polyps to be detected. Results: The performance of the best ANN architecture, trained by using a training set of 27 sessile polyps from the new 43 available dataset, were evaluated in terms of FPs and false-negatives and compared to the results shown in Bevilacqua et al., Three-dimensional Virtual Colonoscopy for Polyps Detection by Supervised Artificial Neural Networks D.-S. Huang et al. (Eds.): ICIC, LNBI 6840, Springer-Verlag, Berlin He

Autore Pugliese

• Bevilacqua Vitoantonio

Tutti gli autori

• BEVILACQUA VITOANTONIO

Titolo volume/Rivista

NEUROCOMPUTING

Anno di pubblicazione

2013

ISSN

0925-2312

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

15

Ultimo Aggiornamento Citazioni

2017-04-23 03:20:56

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile