| |
Records |
Links |
|
Author |
Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov |
|
| |
Title |
Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks |
Type |
Journal |
| |
Year |
2018 |
Publication |
Journal of Medical Robotics Research |
Abbreviated Journal |
JMRR |
|
| |
Volume |
3 |
Issue |
2 |
Pages |
|
|
| |
Keywords |
convolutional neural networks; colonoscopy; computer aided diagnosis |
|
| |
Abstract |
Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp
detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MV; no menciona |
Approved |
no |
|
| |
Call Number  |
BZM2018 |
Serial |
2976 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Maria Vanrell; Jordi Vitria; Xavier Roca |
|
| |
Title |
A multidimensional scaling approach to explore the behavior of a texture perception algorithm. |
Type |
Journal Article |
| |
Year |
1997 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
|
|
| |
Volume |
9 |
Issue |
|
Pages |
262–271 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR;ISE;CIC;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ VVR1997 |
Serial |
35 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
|
| |
Title |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
Type |
Journal Article |
| |
Year |
2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
|
| |
Volume |
29 |
Issue |
2 |
Pages |
246-259 |
|
| |
Keywords |
|
|
| |
Abstract |
Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions. |
|
| |
Address |
|
|
| |
Corporate Author |
IEEE |
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0278-0062 |
ISBN |
|
Medium |
|
|
| |
Area |
800 |
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;MV;OR;SIAI |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
Serial |
1281 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jordi Vitria; Petia Radeva; X. Binefa |
|
| |
Title |
EigenHistograms: using low dimensional models of color distribution for real time object recognition |
Type |
Journal Article |
| |
Year |
1999 |
Publication |
|
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Ljubliana, Slovenia, Springer-Verlag |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR;MILAB;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ VRB1999a |
Serial |
29 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
|
| |
Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
Type |
Journal Article |
| |
Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
27 |
Issue |
8 |
Pages |
875–881 |
|
| |
Keywords |
ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
|
| |
Abstract |
Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
800 |
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;MV;SIAI |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
Serial |
647 |
|
| Permanent link to this record |
