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Both of these methods require manual initialization of eye location.
Fully automated detection and analysis should be feasible for CTCscope, because false positive identification is minimal owing to the high specificity of RNAscope ISH; in contrast, IHC-based detection methods require manual morphological examination to avoid false positives.
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Such a method requires manual adjustment of several thresholds and configurable parameters.
However, MO-PET derives the tumor contour with simple VOI, while the gradient-based segmentation method requires manual adjustment for the tumor contour.
This method requires manual selection of four points for each tissue type for initialization of the algorithm.
However, this method requires manual delineation of seven polygonal regions of interest in the posterior intercostal spaces, using dedicated software in anesthetized and paralyzed patients and only the ratio of densities between each PEEP can be calculated [ 64].
Semi-automated segmentation methods often require manual selection of seeds and many times such selection is performed in a repetitive process to ensure adequacy of seeds.
However, these methods generally require manual input, thereby limiting the throughput of the experiment and increasing the susceptibility to cross-contamination and nonspecific amplification.
Finally, although other methods may require manual specification of an appropriate prior distribution on the number of possible states, taking a non-parametric approach allows our prior distribution to naturally expand to explain the observed data as the size and complexity of the data grows.
Their method did however require manual pre-processing.
Therefore, this method does not require manual tracing of regions.
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