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Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling
of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial
extent of prostate cancer (CaP); the most common source for these labels is expert segmentations. When
ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer
ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual
segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal
image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment
of the two modalities, in order to improve the quality of training data and hence classifier performance. We
quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to
the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels
for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration
methods were affi;ne schemes; one based on maximization of mutual information (MI) and the other method
that we previously developed, Combined Feature Ensemble Mutual Information (COFEMI), which incorporates
high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by
succeeding the two affine registration methods with an elastic deformation step using thin-plate splines (TPS).
In the absence of definitive ground truth for CaP extent on MRI, classifier accuracy was evaluated against 7
ground truth surrogates obtained by different combinations of the expert and registration segmentations. For
26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver
operating characteristic curve compared to that obtained from expert annotation. These results suggest that in
the presence of additional multimodal image information one can obtain more accurate object annotations than
achievable via expert delineation despite vast differences between modalities that hinder image registration.
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