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Producer: Aspose.Pdf for .NET 10.1.0 PDF Creator http://www.pdf-tools.com; modified using iTextSharp™ 5.4.0 ©2000-2012 1T3XT BVBA (AGPL-version) CreationDate: Tue Jul 25 00:42:16 2017 CEST ModDate: Wed Aug 9 09:30:37 2017 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 9 Encrypted: no Page size: 599.98 x 799.99 pts Page rot: 0 File size: 2174200 bytes Optimized: no PDF version: 1.4 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- MinionPro-Regular3 CID Type 0C Identity-H yes no yes 27 0 ELLMON+MinionPro-It Type 1C WinAnsi yes yes no 28 0 ELLMOO+Minion-Black Type 1C WinAnsi yes yes no 29 0 ELLMOP+MinionPro-Bold Type 1C WinAnsi yes yes no 30 0 ELLNAA+MinionPro-Regular2 Type 1C WinAnsi yes yes no 31 0 ELLNAB+MinionPro-Regular+fb Type 1C Custom yes yes yes 32 0 ELLNAC+MinionPro-Regular+20 Type 1C WinAnsi yes yes no 33 0 DFEWRP+MinionPro-Regular CID TrueType Identity-H yes yes yes 1 0 ELLOHK+MinionPro-Regular4 Type 1C WinAnsi yes yes no 75 0 ELLOPI+MinionScUnic Type 1C WinAnsi yes yes no 76 0 ELLPAI+MinionMath-Regular Type 1C WinAnsi yes yes no 133 0 ELMADD+MinionMath-Regular+22 Type 1C Custom yes yes yes 172 0 ELMAEF+MinionPro-It+fb Type 1C Custom yes yes yes 173 0 ELMAEG+MinionMathItalic+03 Type 1C Custom yes yes yes 174 0 ELMFOE+MinionPro-Regular Type 1C WinAnsi yes yes no 271 0 Jhove (Rel. 1.6, 2011-01-04) Date: 2018-06-01 04:39:02 CEST RepresentationInformation: pmc_29065595.pdf ReportingModule: PDF-hul, Rel. 1.8 (2009-05-22) LastModified: 2018-02-08 07:44:24 CET Size: 2174200 Format: PDF Version: 1.4 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 428 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7c53a9eb Item: Title: 1. Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@ed17bee Item: Title: 2. Endoluminal Scene Segmentation Benchmark Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@2a33fae0 Children: Item: Title: 2.1. Dataset Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@707f7052 Item: Title: 2.2. Metrics Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@11028347 Item: Title: 3. Baseline Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@14899482 Item: Title: 4. Experimental Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@21588809 Children: Item: Title: 4.1. Endoluminal Scene Semantic Segmentation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@2aae9190 Children: Item: Title: 4.1.1. Influence of Data Augmentation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@2f333739 Item: Title: 4.1.2. Influence of the Number of Classes Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77468bd9 Item: Title: 4.1.3. Comparison to State-of-the-Art Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12bb4df8 Item: Title: 4.2. Polyp Localization Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4cc77c2e Item: Title: 4.3. Towards Clinical Applicability Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a7b0070 Item: Title: 5. Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@39a054a5 Item: Title: Conflicts of Interest Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@71bc1ae4 Item: Title: Acknowledgments Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6ed3ef1 Info: Title: HDJHE_4037190 1..9 Creator: Aspose Ltd. 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40 LastChar: 233 FontDescriptor: FontName: ELLMON+MinionPro-It Flags: Serif, Nonsymbolic FontBBox: -135, -250, 1000, 854 FontFile3: true Encoding: WinAnsiEncoding Font: BaseFont: ELLMOO+Minion-Black FontSubset: true FirstChar: 44 LastChar: 243 FontDescriptor: FontName: ELLMOO+Minion-Black Flags: Serif, Nonsymbolic, ForceBold FontBBox: -133, -250, 1120, 827 FontFile3: true Encoding: WinAnsiEncoding Font: BaseFont: ELLMOP+MinionPro-Bold FontSubset: true FirstChar: 44 LastChar: 57 FontDescriptor: FontName: ELLMOP+MinionPro-Bold Flags: Nonsymbolic FontBBox: -20, -239, 968, 854 FontFile3: true Encoding: WinAnsiEncoding Font: BaseFont: ELLNAA+MinionPro-Regular2 FontSubset: true FirstChar: 37 LastChar: 250 FontDescriptor: FontName: ELLNAA+MinionPro-Regular2 Flags: Serif, Nonsymbolic FontBBox: -20, -250, 947, 854 FontFile3: true Encoding: WinAnsiEncoding CIDFontType0: Font: BaseFont: MinionPro-Regular3 CIDSystemInfo: Registry: Adobe Registry: Identity Supplement: 0 FontDescriptor: FontName: MinionPro-Regular3 Flags: Nonsymbolic FontBBox: -290, -360, 1684, 989 FontFile3: true CIDFontType2: Font: BaseFont: DFEWRP+MinionPro-Regular CIDSystemInfo: Registry: Adobe Registry: Identity Supplement: 0 FontDescriptor: FontName: DFEWRP+MinionPro-Regular Flags: Nonsymbolic FontBBox: -289, -360, 1684, 917 FontFile2: true XMP: application/pdfA Benchmark for Endoluminal Scene Segmentation of Colonoscopy ImagesVázquez, DavidBernal, JorgeSánchez, F. JavierFernández-Esparrach, GloriaLópez, Antonio M.Romero, AdrianaDrozdzal, MichalCourville, AaronColorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.Journal of Healthcare EngineeringHindawi2017/07/26Research articledoi:10.1155/2017/4037190en Pages: Page: Label: 1 Annotations: Annotation: Subtype: Link Rect: 98, 179, 103, 189 Flags: Print AppearanceDictionary: true Destination: 24 Annotation: Subtype: Link Rect: 244, 110, 260, 120 Flags: Print AppearanceDictionary: true Destination: 18 Annotation: Subtype: Link Rect: 434, 191, 439, 201 Flags: Print AppearanceDictionary: true Destination: 24 Annotation: Subtype: Link Rect: 519, 145, 524, 155 Flags: Print AppearanceDictionary: true Destination: 24 Annotation: Subtype: Link Rect: 431, 133, 436, 143 Flags: Print AppearanceDictionary: true Destination: 24 Annotation: Subtype: Link Rect: 494, 87, 499, 97 Flags: Print AppearanceDictionary: true Destination: 24 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