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

Outline  Introduction  CAD algorithm  Theoretical analysis  Conclusion 2010/3/11 2

Introduction  Lung cancer  Lung Nodules  Colon cancer  Colonic Polyps  Attention and eye fatigue  Accuracy and efficiency 2010/3/11 3

Introduction  CAD methods  Computed tomography images  CT lung nodule detection  CT colonic polyp detection 2010/3/11 4

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

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

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

Outline  Introduction  CAD algorithm  Theoretical analysis  Conclusion 2010/3/11 8

CAD algorithm  Pre-Processing and Segmentation  Gradient Orientation  Surface Normal Overlap  Candidate Lesion Selection 2010/3/11 9

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

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

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

CAD algorithm  Pre-Processing and Segmentation  Gradient Orientation  Surface Normal Overlap  Candidate Lesion Selection 2010/3/11 13

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

CAD algorithm  Pre-Processing and Segmentation  Gradient Orientation  Surface Normal Overlap  Candidate Lesion Selection 2010/3/11 15

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

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

CAD algorithm  Pre-Processing and Segmentation  Gradient Orientation  Surface Normal Overlap  Candidate Lesion Selection 2010/3/11 18

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

CAD algorithm 2010/3/11 20

Outline  Introduction  CAD algorithm  Theoretical analysis  Stochastic Anatomic Shape Model  Model Parameter Estimation  Conclusion 2010/3/11 21

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

Stochastic Anatomic Shape Model 2010/3/11 23 真實的形狀 虛擬的圓形

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

Model Parameter Estimation 2010/3/11 25

Outline  Introduction  CAD algorithm  Theoretical analysis  Conclusion 2010/3/11 26

Conclusion  A novel CAD algorithm  Surface normal overlap method  Theoretical traits  Statistical shape model 2010/3/11 27

Conclusion  Optimized the performance  CT simulations  A per-lesion cross-validation method  Provided a preliminary evaluation 2010/3/11 28

Conclusion  Ultimately envision  The first step in a larger overall detection scheme  Intensive classifier  Decrease the false positives rate 2010/3/11 29

THANK YOU FOR LISTENING. The End 2010/3/11 30