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For pharmacokinetic (PK) analysis of Dynamic Contrast Enhanced (DCE) MRI the arterial input function
needs to be estimated. Previously, we demonstrated that PK parameters have a significant better discriminative
performance when per patient reference tissue was used, but required manual annotation of reference tissue. In
this study we propose a fully automated reference tissue segmentation method that tackles this limitation. The
method was tested with our Computer Aided Diagnosis (CADx) system to study the effect on the discriminating
performance for differentiating prostate cancer from benign areas in the peripheral zone (PZ).
The proposed method automatically segments normal PZ tissue from DCE derived data. First, the bladder
is segmented in the start-to-enhance map using the Otsu histogram threshold selection method. Second, the
prostate is detected by applying a multi-scale Hessian filter to the relative enhancement map. Third, normal
PZ tissue was segmented by threshold and morphological operators. The resulting segmentation was used as
reference tissue to estimate the PK parameters. In 39 consecutive patients carcinoma, benign and normal tissue
were annotated on MR images by a radiologist and a researcher using whole mount step-section histopathology
as reference. PK parameters were computed for each ROI. Features were extracted from the set of ROIs using
percentiles to train a support vector machine that was used as classifier. Prospective performance was estimated
by means of leave-one-patient-out cross validation. A bootstrap resampling approach with 10,000 iterations was
used for estimating the bootstrap mean AUCs and 95% confidence intervals.
In total 42 malignant, 29 benign and 37 normal regions were annotated. For all patients, normal PZ was
successfully segmented. The diagnostic accuracy obtained for differentiating malignant from benign lesions using
a conventional general patient plasma profile showed an accuracy of 0.64 (0.53-0.74). Using the automated
per-patient calibration method the diagnostic performance improved significantly to 0.76 (0.67-0.86, p=0.017) ,
whereas the manual per-patient calibration showed a diagnostic performance of 0.79 (0.70-0.89, p=0.01).
In conclusion, the results show that an automated per-patient reference tissue PK model is feasible. A
significantly better discriminating performance compared to the conventional general calibration was obtained
and the diagnostic accuracy is similar to using manual per-patient calibration.
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