TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 1/38 The Effects of Filtering on Visualization and Detection of Colonic Polyps in Ultra Low Dose Multi-Detector CT Data Gert Schoonenberg Biomedical Engineering student, TU/e Final presentation Supervisors: Roel Truyen, Anna Vilanova and Frans Gerritsen Project period: –

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 2/38 Overview Motivation: colorectal cancer Screening methods Research questions Dose in Computed Tomography (CT) Filtering Computer-aided polyp detection Visualization Conclusion Time for questions

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 3/38 Colorectal cancer For industrialized countries: Second leading cause of cancer-related death Accounts for 10% of all cancer mortality For the Netherlands: Causes each year over 9,100 new cases Causes each year over 4,400 deaths Accounts for 3% of all deaths

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 4/38 Screening for colorectal cancer Colorectal cancer: High prevalence Long asymptomatic premalignant phase Well treatable when detected early  Suitable disease for screening

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 5/38 Outcomes of screening Target condition presentabsent Diagnostic result positiveTP (true positive)FP (false positive) negativeFN (false negative)TN (true negative)

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 6/38 Screening methods Imaging techniqueOther technique ScannerX-ray (DCBE) EndoscopeProteomicsFecal tests CT MR Sigmoidoscopy Colonoscopy Blood (FOBT) DNA Screening methods

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 7/38 Research questions Is computer-aided polyp detection still possible if the dose is reduced? Can the artifacts caused by noise in endoluminal visualizations be reduced?  Investigate the use of noise reduction techniques.

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 8/38 Data & CT effective dose DescriptionEffective dose (mSv) Comparable to natural background radiation for CT abdomen (2 scans, 70 mAs years CT abdomen (2 scans, 6.25 mAs months CT abdomen (2 scans, 1.39 mAs 0.17< 1 month Mammography0.73 months Chest X-ray0.110 days Coast-to-coast round trip in the US days

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 9/38 Filtering Gaussian filtering Bilateral filtering

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 10/38 Gaussian filter

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 11/38 Bilateral filter

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 12/38 Results filter methods Gaussian filtering Scale = 2.0 mm Bilateral filtering Scale = 2.95 mm Scale = 250 HU

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 13/38 Computer-aided polyp detection Algorithm developed by Simona Grigorescu and Joost Peters, Advanced Development, Healthcare IT, Philips Medical Systems Best

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 14/38 Polyp detection overview Three steps: Colon segmentation Polyp detection: identification and detection Polyp classification –Bounding box –Linear classifier Colon segmentation

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 15/38 PD: identification and detection Detection step: shape based –All regions with high curvature are selected –Features are calculated for shell volume –Features are calculated for core volume air tissue polyp high curvature shell core colon wall

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 16/38 PD: Classification Feature selection for bounding box –Purpose: discarding outliers. –Select only those features for which the polyp class is compact. –Select only those features that really discard FP. Feature 1 Feature 3 Feature 2 Feature 3 Not a polyp Polyp

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 17/38 PD: Classification Feature selection for linear classifiers (outliers unwanted) –Rank features according to their Gaussianity (minimal overlapping). –Forward selection of features which increase the cluster separability with a minimal value. bad good Not a polyp Polyp

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 18/38 Training Detection step –detect all candidates and calculate shell features and core features. Bounding box –find a minimal cube in the feature space that contains all polyp examples to get rid of outliers. Linear classifier –find a linear boundary between two classes based on the examples (without outliers).

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 19/38 Testing Detection step –Detect all candidates and calculate shell features and core features. Bounding box –Bounding Box: Select only candidates inside the hypercube. Linear classifier –Classification: Classify candidates.

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 20/38 Experiment 1 Test performance of detection step using: Bilateral filtered data Unfiltered data All parameters in the CAD algorithm are kept constant.

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 21/38 Results: detection step Bilateral filtering  33% FP reduction  2.2% decrease in sensitivity (polyps > 6 mm) Dose levelFilteringSensitivityFP NormalNone95%134 LowNone95%163 Ultra lowNone95%312 NormalBilateral93%97 LowBilateral92%109 Ultra lowBilateral93%201

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 22/38 Feature selection for classification Experiment 2a: Optimal feature set for each dose level Experiment 2b: Optimal feature set for normal dose trained on normal dose and tested on all dose levels Experiment 2c: Robust feature set trained on normal dose and tested on all dose levels

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 23/38 Results 2a: optimal features Bounding Box Average false positive reduction is 67% No loss in sensitivity for polyps 6 mm and larger In parenthesis () the results of the detection step are given. Dose levelFilteringSensitivityFP NormalNone 100% (95%)50 (134) LowNone100% (95%)60 (163) Ultra lowNone100% (95%)71 (312) NormalBilateral100% (93%)40 (97) LowBilateral100% (92%)28 (109) Ultra lowBilateral100% (93%)66 (201)

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 24/38 Results: linear classifier

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 25/38 Results: linear classifier

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 26/38 Results 2b: Normal dose features Bounding box Optimal feature set for normal dose trained on normal dose and tested on all dose levels. Decrease of sensitivity on lower dose levels! In parenthesis () the results of the detection step are given. Dose levelFilteringSensitivityFP NormalNone 100% (95%)50 (134) LowNone56% (95%)50 (163) Ultra lowNone12% (95%)44 (312) NormalBilateral100% (93%)40 (97) LowBilateral85% (92%)38 (109) Ultra lowBilateral9% (93%)28 (201)

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 27/38 Result: linear classifier

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 28/38 Results 2c: Robust features Bounding box Not filtered data: Sensitivity: 96%, average FP reduction: 30% Bilateral filtered data: Sensitivity: 98%, average FP reduction: 40% In parenthesis () the results of the detection step are given. Dose levelFilteringSensitivityFP NormalNone 100% (95%)50 (134) LowNone96% (95%)50 (163) Ultra lowNone93% (95%)44 (312) NormalBilateral100% (93%)40 (97) LowBilateral100% (92%)38 (109) Ultra lowBilateral95% (93%)28 (201)

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 29/38 Results: linear classifier

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 30/38 Visualization

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 31/38 Perspective ray-casting

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 32/38 Current visualization Normal dose Smooth surface Low dose Blobs appear Normal dose Rough surface

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 33/38 Proposed solutions Bilateral filtering  blobs Gradient smoothing  rough surface

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 34/38 Results: normal dose

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 35/38 Results: all dose levels 1.6 mAs 6.25 mAs64 mAs

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 36/38 Conclusions Very low dose: filtering before rendering is required! V The new rendering (gradient smoothing) gives similar renderings for low and high dose. V With the new rendering the wall appears relatively smooth when it in fact should be smooth. X No smoothing of important structures. The noise level changes within a dataset. In the really noisy regions strong filtering is needed and smoothing occurs. [New scanners: dose modulation]

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 37/38 Overall conclusion Visualization and computer-aided detection of colorectal polyps is feasible on ultra low dose CT colonography data. It is also possible to create one visualization algorithm and one computer-aided detection algorithm that can cope with various dose levels.

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 38/38 Time for questions Questions?

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 39/38 Current visualization Problem: Bumpy colon surface. Cause: Not the isosurface location, but surface normals. Data: 64 mAs data pelvic region

TU/e PHILIPSPhilips Medical Systems Healthcare IT - Advanced Development 40/38 Simulating low dose q: ratio of actual and desired mAs level poidev: Poisson distribution n 0 : detected photons n 1 : simulated amount of photons