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  • Slide 1
  • Surface Normal Overlap: A Computer-Aided Detection Algorithm With Application to Colonic Polyps and Lung Nodules in Helical CT Authors: David S. Paik*, Christopher F. Beaulieu, Geoffrey D. Rubin, Burak Acar, R. Brooke Jeffrey, Jr., Judy Yee,Joyoni Dey, and Sandy Napel Source: IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23, NO. 6, JUNE 2004 Speaker: Wen-Ping Chuang Adviser: Ku-Yaw Chang 2010/3/11 1
  • Slide 2
  • Outline Introduction CAD algorithm Theoretical analysis Conclusion 2010/3/11 2
  • Slide 3
  • Introduction Lung cancer Lung Nodules Colon cancer Colonic Polyps Attention and eye fatigue Accuracy and efficiency 2010/3/11 3
  • Slide 4
  • Introduction CAD methods Computed tomography images CT lung nodule detection CT colonic polyp detection 2010/3/11 4
  • Slide 5
  • Introduction 2010/3/11 5 Detecting lung nodulesSensitivityFPs 2D multilevel thresholding detection algorithm 94%1.25 Multilevel thresholding and a rolling ball algorithm 70%1.5 Patient-specific models86%11 An improved template-matching technique72%31
  • Slide 6
  • Introduction 2010/3/11 6 Detecting colonic polypsSensitivityFPs Measures abnormal wall thicknesses73%9-90 Convolution-based partial derivatives64%3.5 Both prone and supine datasets100%2.0 Combined surface normal and sphere fitting methods 100% 8.2
  • Slide 7
  • Introduction Surface normal overlap method On 8 CT datasets 2010/3/11 7 DetectionSizeSensitivityFPs Colonic polyps10mm and larger100%7.0 Lung nodules6mm and larger90%5.6
  • Slide 8
  • Outline Introduction CAD algorithm Theoretical analysis Conclusion 2010/3/11 8
  • Slide 9
  • CAD algorithm Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection 2010/3/11 9
  • Slide 10
  • Pre-Processing and Segmentation CT volume data I(x,y,z) (0.6mm) 3 Reduce any bias Lesions at different orientations Datasets with different voxel sizes Segmentation automatically Colon lumen Lung parenchyma 2010/3/11 10
  • Slide 11
  • Pre-Processing and Segmentation Segmentation automatically (S1) All air intensity voxels I(x,y,z) -700HU Negatively any data volume connected to the edges width or depth of greater than 60 mm small air pockets 2010/3/11 11
  • Slide 12
  • Pre-Processing and Segmentation Segmentation automatically (S2) Limit the remaining computations reduces computational requirements eliminates FPs arising within soft tissue structures Produce a 5mm thickened region 2010/3/11 12
  • Slide 13
  • CAD algorithm Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection 2010/3/11 13
  • Slide 14
  • Gradient Orientation Computes the image gradient vector High-contrast edges Determine the image surface normals Reduced search space Resulting surface normal vectors 2010/3/11 14
  • Slide 15
  • CAD algorithm Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection 2010/3/11 15
  • Slide 16
  • Surface Normal Overlap Critical for detecting lesions Convex regions and surfaces Surface normal vectors A dominant curvature along a single direction polyps and nodules Set 10mm of the projected surface normal vectors 2010/3/11 16
  • Slide 17
  • Surface Normal Overlap Robustness Perfectly spherical objects Radial direction allowing roughly globular objects to have a significant response Transverse direction allowing nearly intersect surface normal vectors to be additive 2010/3/11 17
  • Slide 18
  • CAD algorithm Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection 2010/3/11 18
  • Slide 19
  • Candidate Lesion Selection Complex anatomic structures Multiple convex surface patches Multiple local maxima Smallest scale of the features Generate distinct local maxima Set to 10 mm Sorted in decreasing order and recorded 2010/3/11 19
  • Slide 20
  • CAD algorithm 2010/3/11 20
  • Slide 21
  • Outline Introduction CAD algorithm Theoretical analysis Stochastic Anatomic Shape Model Model Parameter Estimation Conclusion 2010/3/11 21
  • Slide 22
  • Stochastic Anatomic Shape Model A simple parametric shape Add stochastically-governed variation Produce realistic anatomic shape Nominal position Radius is random variables 2010/3/11 22
  • Slide 23
  • Stochastic Anatomic Shape Model 2010/3/11 23
  • Slide 24
  • Model Parameter Estimation Performing edge detection Identifying the surface normal vectors nodule, polyp, vessel, fold Finding the nominal sphere or cylinder 2010/3/11 24
  • Slide 25
  • Model Parameter Estimation 2010/3/11 25
  • Slide 26
  • Outline Introduction CAD algorithm Theoretical analysis Conclusion 2010/3/11 26
  • Slide 27
  • Conclusion A novel CAD algorithm Surface normal overlap method Theoretical traits Statistical shape model 2010/3/11 27
  • Slide 28
  • Conclusion Optimized the performance CT simulations A per-lesion cross-validation method Provided a preliminary evaluation 2010/3/11 28
  • Slide 29
  • Conclusion Ultimately envision The first step in a larger overall detection scheme Intensive classifier Decrease the false positives rate 2010/3/11 29
  • Slide 30
  • THANK YOU FOR LISTENING. The End 2010/3/11 30

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