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Segmentation of vessel structures in serial whole slide sections using region-based context features
In this work, we present a fast and robust spatial alignment framework, which combines automated breast segmentation and current-prior registration techniques in a multi-level fashion. First, fully automatic breast segmentation is applied to extract the breast masks that are used to obtain an initial affine transform. Then, a non-rigid registration algorithm using normalized gradient fields as similarity measure together with curvature regularization is applied. A total of 29 subjects and 58 breast MR images were collected for performance assessment. To evaluate the global registration accuracy, the volume overlap and boundary surface distance metrics are calculated, resulting in an average Dice Similarity Coefficient (DSC) of 0.96 and root mean square distance (RMSD) of 1.64 mm. In addition, to measure local registration accuracy, for each subject a radiologist annotated 10 pairs of markers in the current and prior studies representing corresponding anatomical locations. The average distance error of marker pairs dropped from 67.37 mm to 10.86 mm after applying registration.
In this work, the clinical value of the proposed assessment software is evaluated. In a retrospective clinical study, 39 cases of hepatic tumor ablation are evaluated using the prototype software and conventional image comparison by four radiologists with different levels of experience. The cases are randomized and evaluated in two sessions to avoid any recall-bias. Self-confidence of correct diagnosis (local recurrence vs. no local recurrence) on a six-point scale is given for each case by the radiologists. Sensitivity, specificity, positive and negative predictive values as well as receiver operating curves are calculated for both methods. It is shown that the software-assisted method allows physicians to correctly identify local tumor recurrence with a higher percentage than the conventional method (sensitivity: 0.6 vs. 0.35), whereas the percentage of correctly identified successful ablations is slightly reduced (specificity: 0.83 vs. 0.89).
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