CT colonography: advanced computer-aided detection scheme utilizing MTANNs for detection of "missed" polyps in a multicenter clinical trial

Abstract

Purpose: The purpose of this study was to develop an advanced computer-aided detection (CAD) scheme utilizing massive-training artificial neural networks (MTANNs) to allow detection of "difficult" polyps in CT colonography (CTC) and to evaluate its performance on false-negative (FN) CTC cases that radiologists "missed" in a multicenter clinical trial.

Methods: The authors developed an advanced CAD scheme consisting of an initial polyp-detection scheme for identification of polyp candidates and a mixture of expert MTANNs for substantial reduction in false positives (FPs) while maintaining sensitivity. The initial polyp-detection scheme consisted of (1) colon segmentation based on anatomy-based extraction and colon-based analysis and (2) detection of polyp candidates based on a morphologic analysis on the segmented colon. The mixture of expert MTANNs consisted of (1) supervised enhancement of polyps and suppression of various types of nonpolyps, (2) a scoring scheme for converting output voxels into a score for each polyp candidate, and (3) combining scores from multiple MTANNs by the use of a mixing artificial neural network. For testing the advanced CAD scheme, they created a database containing 24 FN cases with 23 polyps (range of 6-15 mm; average of 8 mm) and a mass (35 mm), which were "missed" by radiologists in CTC in the original trial in which 15 institutions participated.

Results: The initial polyp-detection scheme detected 63% (15/24) of the missed polyps with 21.0 (505/24) FPs per patient. The MTANNs removed 76% of the FPs with loss of one true positive; thus, the performance of the advanced CAD scheme was improved to a sensitivity of 58% (14/24) with 8.6 (207/24) FPs per patient, whereas a conventional CAD scheme yielded a sensitivity of 25% at the same FP rate (the difference was statistically significant).

Conclusions: With the advanced MTANN CAD scheme, 58% of the polyps missed by radiologists in the original trial were detected and with a reasonable number of FPs. The results suggest that the use of an advanced MTANN CAD scheme may potentially enhance the detection of "difficult" polyps.

Figure 1

Distributions of sizes of FN polyps in the entire trial and in the database used in this study. Note the two different scales on the left and right vertical axes.

Figure 2

Flowchart of our CAD scheme utilizing 3D MTANNs for the detection of polyps∕masses in CTC.

Figure 3

Schematic illustration of the principles of a 3D MTANN for distinguishing polyps∕masses from FPs. The 3D MTANN was trained to enhance lesions and suppress nonlesions. Lesions such as a sessile polyp, a sessile polyp on a fold, and a mass are enhanced in the output images, whereas nonpolyps such as a rectal tube, stool, and the ileocecal value (ICV) are suppressed. By the use of a scoring scheme, each of the output images is converted to a single score, indicating the likelihood of being a lesion for each lesion candidate. Classification between lesions and nonlesions is made by thresholding of the likelihood scores.

Figure 4

FROC curves for the performance of our CAD scheme utilizing 3D MTANNs and that of the conventional LDA CAD scheme for the 14-case polyp-visible-on-both-views subdatabase and the whole 24-case database. Our scheme achieved 58% sensitivity with 8.6 FPs∕patient for 24 polyps∕mass missed by reporting radiologists in the original clinical trial. The error bars indicate 95% confidence intervals.

Figure 5

Illustrations of polyps missed by reporting radiologists during initial reading in the original trial in 2D views (upper images) and 3D endoluminal views (lower images), which were detected by our MTANN CAD scheme. (a) A small polyp (6 mm; hyperplastic) in the sigmoid colon was detected correctly by our CAD scheme (indicated by an arrow). This polyp was missed in both CTC and reference-standard optical colonoscopy in the original trial. (b) A small polyp (6 mm; adenoma) in the sigmoid colon. (c) A sessile polyp on a fold (10 mm; adenoma) in the ascending colon.

Figure 6

Illustrations of polyps missed by reporting radiologists during initial reading in the original trial in 2D views (upper images) and 3D endoluminal views (lower images), which were not detected by our CAD scheme. (a) A sessile polyp on a fold (12 mm; adenoma) in the descending colon. (b) A small sessile polyp on a fold (6 mm; hyperplastic) in the cecum.

Figure 7

Illustrations of FPs by our CAD scheme, which were categorized by subjective grading of ease. Moderate cases: (a) Stool and (b) collapsed colon segment and a fold. Difficult cases: (c) Stool and (d) a hemorrhoid.