As with the prostate, the lowest TRE values among the automated methods were achieved by ESA on the lower resolution and MIM on the high resolution data with RVSS being the third best method. The other methods reached TRE values comparable to each other. In terms of maximum TRE and ATRE, the conclusion was less clear. Voloom performed better on the lower resolution, reaching a maximum TRE second only to LS, while ESA and OPT also reached comparable values. On this dataset, MIM suffered from larger maximum errors compared to the higher quality prostate sample. The lowest mean ATRE values among all automated methods were obtained by ESA, MIM and Voloom, while in terms of maximum ATRE Voloom was superior to ESA and MIM. ESA was the top method in terms of RMSE and f 2, and MIM obtained the highest Jaccard index. Again, the poorest results were obtained when using the default values of tunable parameters. Plus, VOLOOM has protective ceramic coated plates, as well as ionic technology that help to seal the cuticle and protect from damage. All of these features protect the hair. OPT: Optimization-based reconstruction implemented in MATLAB R2016b was used to estimate pairwise affine transformations by minimizing the value of pixel-wise MSE.

Fiji ( Schindelin et al., 2012; Schneider et al., 2012) (v. 1.51h) plugins were run via ImageJ-MATLAB interface (v. 0.7.1) ( Hiner et al., 2016). Transformations were re-applied to the mask and landmark images. Output was saved as TIF. See Supplementary Methods for details. The two samples selected for this study are markedly different in their histological composition. The fact that the top methods performed well on both the prostate and the liver dataset without any retuning of parameters indicates that these methods are not overly sensitive to tissue appearance, and that the results obtained in this study are not specific to a single dataset. However, some variation in the relative performance of the algorithms on the two datasets was still observed. Thus, collecting and annotating additional datasets representing diverse tissue types and other histological stainings, such as immunohistochemistry, remains an important goal for future studies. We evaluated possible connections between accuracy and computation time, which might require the user to make a trade-off when selecting parameters (see Supplementary Results). The time taken by OPT varied only by a few minutes, except for the single inaccurate solutions where the parameters have not allowed proper convergence of the algorithm. For SIFT, there were no signs of a connection between accuracy and computation time. The differences in computation time between the fastest and slowest iterations of RVSS were roughly twofold and the fastest iterations were generally the ones with the highest error, indicating that minimizing the computation time of RVSS would sacrifice accuracy. In the case of ESA, the effect of parameter tuning was dramatic, leading to variation from approximately 12 min to more than 41 h. However, any clear relationship between computation time and accuracy was not observed. 3.3 Comparison of algorithms based on the prostate dataset

First, make sure your hair is dry and styled as you like. (VOLOOM can be used on hair that is freshly styled or on 2nd or 3rd day hair). Part your hair normally.

Repeat this process as you move VOLOOM down the hair shaft, two to three times, stopping at about eye or cheekbone level. You can experiment with more or less, depending on the length of your hair. All methods benefited from parameter tuning on both image resolutions based on most of the metrics, using either set of landmarks for evaluation (see Table 1 and Supplementary Results). Of the top three methods, MIM and RVSS obtained better accuracy using high resolution images and ESA worked better on the low resolution images. ESA and MIM reached similar mean TRE values, slightly better than RVSS and approaching or exceeding the accuracy of LS. In terms of maximum TRE and ATRE, the three methods were comparable, but RVSS reached slightly lower ATRE than ESA or MIM. Among all tools, ESA and MIM also obtained the highest Jaccard index values. The RMSE and f 2 metrics do not allow comparison across different image resolutions and one should note that MIM’s output was always stored at the lower resolution for technical reasons. Considering these limitations, we can observe that ESA performed best in terms of these metrics on both image resolutions ahead of RVSS. Changes in tissue area introduced by ESA, MIM and RVSS were moderate. Behind the top three, most other tools reached accuracy comparable to each other. The worst results were obtained using default parameters and for some methods, most notably ESA and RVSS, they were even comparable to the unregistered original images.VOLOOM is made to be used on the under-layers of hair, which are covered by an untreated top layer. Part your hair normally, and then section off the top layer that you would like to stay smooth, and clip it off to the side. This top layer of hair should be about ½ to 1 inch wide and run parallel to your regular part. This layer will stay smooth and untreated. You will also want to make sure that a small section of hair – about ½ to 1 inch wide -- running alongside your face stays smooth and untreated. Based on this study, methods utilizing locally varying transformations (ESA, MIM, RVSS, Voloom) were superior to those constrained to global affine models (OPT, SIFT, HSR). ESA was the only method to consistently outperform or match the other approaches on two datasets based on the majority of metrics. In the case of the higher quality prostate dataset, differences in accuracy between the tools were rather subtle. All three top-performing methods on this dataset incorporate an elastic transformation model: MIM and RVSS use a B-spline grid and ESA is based on a piecewise linear mesh. While methods relying on a global transformation model also performed reasonably well, the additional accuracy offered by elastic transformations could be crucial when microstructure at the cellular scale is of interest. In the case of the liver sample, more profound differences between the methods were observed, likely due to the more challenging tissue content and the presence of deformations, which cannot be compensated for using a global model. ESA, MIM and Voloom stood out from the other methods. While Voloom appeared to be less accurate on average compared to ESA and MIM based on mean TRE, it demonstrated the lowest maximum and accumulated errors of all automated methods, indicating capability to avoid propagation of errors even in the presence of considerable deformations. The ability of the algorithms to tolerate such deformations is a significant benefit. Due to the mostly manual nature of histological sectioning and brittleness of the thin tissue sections, deformations in the form of folds and tears often occur. This challenge is especially encountered in 3D histology, when uninterrupted sequences of sections are desired.