Variability of LIDC Panel Segmentations and Soft Segmentation of Lung Nodules

Presented by Stephen Siena and Olga Zinoveva

OverviewDiscussion of LIDC dataNew variability metricSoft segmentation

◦Related work◦Methods◦Discussion

Future Work

The LIDCChest CT scans reviewed by 4

radiologists◦Semantic characteristics and contours

Benefits to research◦Access for everyone◦Sets standard

Problem: No ground truth◦No perfect detection/outline of nodules

Our Proposed SolutionFind “accuracy” of radiologists

first

Provides measure of panel segmentations’ consistency◦Validation of reference truth

Incorporates and improves upon previous metrics

Methodology

• Cost matrix

• Variability matrix

max( ) ( , )( 1) ( , ) 0

max( ) 1( , )

( , ) 0

P P r cR if P r c

PC r c

k if P r c

* ( , ) ( , ) * ( , )( , )

( , ) ( , ) * ( , )

v C r c if V r c v C r cV r c

V r c if V r c v C r c

MethodologyVariability index

Normalized variability index

),( crvVI

4

),(

crPVI

VIn

(a) Radiologist outlines

(b) Pmap

(c) Cost matrix (R = 4; k = 10)

(d-g) Variability matrix after 0, 1, 2, 3 iterations

(h)Final variability matrix

VI = 60; VIn = 5.1064

Results

VIn = .5227 VIn = 1.8198

VIn = 15.1705 VIn = 37.8774

VIn = 3.2339

VIn = 7.0345

Complements Overlap

Overlap = .2245VIn = 11.6000

Overlap = .2246VIn = 35.4449

Overlap = .2064VIn = 81.4449

Overlap = .2763VIn = 12.3711

Overlap = .4462VIn = 12.5101

Overlap = .6771VIn = 12.5896

BackgroundBackgroundMost lung nodule segmentation

algorithms produce hard segmentations

Probabilistic segmentations used for other medical imaging purposes◦Cai, Hongmin et al. Produced brain

segmentations◦Tang, Hui – kidney segmentations

van Ginneken, Bram produced lung nodule segmentations on the first LIDC dataset

Dataset and pre-Dataset and pre-processingprocessingAll slices from the LIDC 85

dataset that contain four radiologist contours◦264 slices representing 39 nodules

Different CT scanners convert HU to intensity differently◦Solution – intensity shift based on

the rescale intercept

Random point selectionRandom point selection

0%, 25%, 50%, 75%, 100%

Points selected proportionately from every region and every image.

Random point selectionRandom point selectionCoordinates selected randomly,

but must be at least R pixels away from each other for any region

inT

ins

PR

2× P -0.1

PinT is the total number of pixels in agreement area i of image nPins is the number of pixels selected from agreement area i of image n

ClassifierClassifierIntensity and texture (Gabor and

Markov) features calculated for a 9X9 neighborhood around each pixel

Classifier assigned a continuous probability (0-100) for each pixel’s membership in the nodule class

These values were thresholded to produce a p-map of the segmentation

Classifier resultsClassifier resultsMedian soft overlap: 0.53Median VI: 4.24 (Q1:2.5, Q2:9.8)Chest wall causes the majority of errorsOver-segmentation on most slices

Classifier resultsClassifier results

Nodule Radiologist p-map Classifier’s p-map

Post-processing: VI Post-processing: VI TrimmingTrimming

Terminate

VI Trimming: ExampleVI Trimming: Example

0 3 3

0 4 3

0 1 2

10 1 1

10 0 1

10 3 2

1 2 3 2 1

2 0 0 0 2

3 0 0 0 3

2 0 0 0 2

1 2 3 2 1

0 1 1 1 2

0 0 3 3 2

0 0 4 3 2

0 0 1 2 2

0 0 0 0 1

14 4 4 4 3

14 10 1 1 3

20 10 0 1 3

20 10 3 2 3

20 13 12 12 5

1 2 3 3 3 2 1

2 0 0 0 0 0 2

2 0 0 0 0 0 3

1 0 0 0 0 0 3

0 0 0 0 0 0 3

0 0 0 0 0 0 2

0 1 2 3 3 2 1

Results after VI trimmingResults after VI trimmingMedian soft overlap: 0.57 (vs.

0.53)Median RAE: 9.9 (vs. 17.5)

Results after VI trimmingResults after VI trimming

Ongoing and future workOngoing and future workImprove segmentation of nodules

attached to chest wallsSelect seed points without manual

inputCalculate VI for the segmentationsWork with all slices for the 39 nodulesExpand to the new LIDC datasetSegment lungs

◦Eliminates the need for chest wall separation algorithms

◦Allows for better intensity normalization

ReferencesReferencesCai, Hongmin et al. “Probabilistic

Segmentation of Brain Tumors Based on Multi-Modality Magnetic Resonance Images,” 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 600-603 (April 2007)

Tang, Hui et al. “A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model,” Computerized Medical Imaging and Graphics (August 2009)

van Ginneken, Bram, “Supervised Probabilistic Segmentation of Pulmonary Nodules in CT Scans,” MICCAI 2006 Proceedings, Part II pp. 912-919 (October 2006)

Questions?