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. Here’s a basic guide. But experiment to find your ideal temperature. The digital controls and readout make finding and choosing the right temperature so easy and accurate. Remember, the first time you use the VOLOOM hair volumizer, select a lower temperature than you think you might need, until you get the hang of the technique! If you just don’t like a lot of heat on your hair, you always have the option of selecting a lower temperature and holding VOLOOM on for slightly longer. Just experiment to find out what temperature works best for your particular hair type. 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.

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, we analyzed whether our metrics depend on image resolution (see Supplementary Results). TRE, ATRE, Jaccard and ΔA-% are essentially invariant to image resolution. They can be compared across different datasets and resolutions, as long as the accumulation of interpolation errors is avoided. RMSE, NCC, MI, NMI, f 2 and f 3 depend both on resolution and image content, and these metrics should thus only be compared within the same dataset and resolution. In all following analyses, we used images subsampled to pixel sizes of 7.36 and 3.68 µm, referred to as low and high resolution, respectively. The pixel sizes are close to the 5 µm section spacing and metrics computed from these images are not distorted by interpolation errors. Furthermore, we will only present RMSE as a measure of pixelwise similarity and f 2 as a measure of reconstruction smoothness due to their strong correlations with NCC, MI, NMI and f 3 (see Supplementary Table S1 for details). 3.2 Automated parameter tuningVOLOOM 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. The optimization mostly converged close to the final solution in a handful of iterations (see Supplementary Results). By inspecting the variation in mean TRE values obtained during the process it is possible to reach a semi-quantitative view of the sensitivity of each method towards parameter adjustments. OPT and SIFT produced similar results for most parameter combinations while ESA, MIM and especially RVSS exhibited more sensitivity to parameter tuning. VOLOOM has been designed to help you achieve maximum results while minimizing the potential for hair damage. It is to be used only on the hair near the scalp and a few inches down the hair shaft. This hair is rich in natural protective oils – your own natural heat protection. Unlike other hot tools, it is never used on the ends of hair, most prone to damage. This work was supported by Academy of Finland [269474]; Tekes [269/31/2015]; Cancer Society of Finland; Emil Aaltonen Foundation; Finnish Foundation for Technology Promotion; KAUTE Foundation; and Orion Research Foundation.

LS: Least-squares fitting of an affine transformation to the landmarks was implemented in MATLAB R2016b. The result is in principle unaffected by error accumulation ( Xu et al., 2015).Digitalization of pathology has been accelerated by improvements in technology allowing acquisition of whole slide images (WSI) ( Ghaznavi et al., 2013; Griffin and Treanor, 2017). Besides computer-aided facilitation of pathologists’ tasks, digital pathology can enable new approaches like 3D histology, where three-dimensional reconstructions of samples are formed in silico based on serial sections ( Magee et al., 2015; Roberts et al., 2012). While other techniques allow imaging directly in 3D, they are currently incapable of matching the subcellular resolution and throughput of whole slide imaging. Examples of potential applications include construction of data-driven computer models and improved diagnostics of diseases associated with changes in the 3D microarchitecture of tissue. Moreover, 3D histology is compatible with established histopathological interpretation techniques and biochemical assays such as immunohistochemistry or in situ hybridization. This raises interesting prospects in view of recent advances in spatially resolved omics ( Mignardi et al., 2017; Ståhl et al., 2016). Pairing imaging with genomic, epigenomic, transcriptomic and proteomic data in the spatial context of tissue holds great promise for pathology and other fields ( Koos et al., 2015). Taking a step further, this could be performed in 3D to truly probe the relationships between structural and functional features as well as the heterogeneity and interplay between different cell types in tumors, and significant projects are now pursuing these goals ( Ledford, 2017; Rusk, 2016). These kind of approaches have already led to the creation of brain atlases ( Amunts et al., 2013; Johnson et al., 2010; Lein et al., 2007). Such high-dimensional data also represent an exciting challenge for new ways of scientific visualization based e.g. on virtual reality techniques ( Calì et al., 2016; Ledford, 2017; Theart et al., 2017). 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. In the case of MIM, which had to be operated interactively, we evaluated each combination of tunable values by a parameter sweep. Tunable parameters of the other methods were optimized via Bayesian optimization ( Shahriari et al., 2016; Snoek et al., 2012), which is well-suited for such problems, where the objective function is computationally expensive to evaluate, nonconvex, multimodal, and typically has low to moderate dimensionality. Bayesian optimization has been shown to perform favorably in comparison to other global optimization algorithms on benchmarking functions ( Jones, 2001) as well as on real WSI data ( Teodoro et al., 2017). We used MATLAB’s bayesopt implementation ( https://www.mathworks.com/help/stats/bayesian-optimization-algorithm.html) with mean pairwise TRE as the objective function. We utilized a Gaussian process model of the objective function and an automatic relevance determination (ARD) Matérn 5/2 kernel ( Snoek et al., 2012) with ‘expected-improvement-plus’ as the acquisition function ( Bull, 2011). Reconstructions with output image dimensions over fivefold compared to the input due to extreme error accumulation were considered failures. The number of variables to optimize was 2 (OPT), 4 (SIFT), 7 (RVSS) or 15 (ESA). We first optimized SIFT alone and used the optimal values for the SIFT step of RVSS and ESA. See Supplementary Table S1 for descriptions of the parameters. The number of seed points was set to twice the number of variables. We ran 30 iterations for OPT due to its simple objective function ( Kartasalo et al., 2016) and 100 iterations for the other tools. We used the prostate images subsampled by factors of 8 and 16, except for ESA, for which optimization was only feasible using the factor 16. Parameters optimized for ESA using the lower resolution were scaled to be used with the high resolution images. Computations were run on a workstation with Intel Xeon E5-1660 v3 3 GHz and 64 GB of RAM (low resolution) and a cluster node with Intel Xeon E5-2680 v3 2.5 GHz and 128 GB of RAM (high resolution). 3 Results 3.1 Effect of image resolution on evaluation metrics 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.

Of the evaluated methods, LS, HSR and Voloom do not have tunable parameters. For OPT, SIFT, RVSS, ESA and MIM, we tuned the parameters automatically, minimizing the mean TRE computed for the prostate dataset. Parameter optimization took approximately 1500 hours in total to compute, producing 23 terabytes of data. Most women over-wash and over-style their hair because it falls flat if they don’t. With VOLOOM, you’ll likely find that you can easily go extra days without washing because your hair is never flat! This change in your regular hair routine means that you’ll find that you need to shampoo, color, and heat-style your hair less often with VOLOOM, causing less damage to your hair over time. 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. 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.OPT: Optimization-based reconstruction implemented in MATLAB R2016b was used to estimate pairwise affine transformations by minimizing the value of pixel-wise MSE. VOLOOM’s digital controls and readout give you complete control over temperature, which can be adjusted every five degrees, starting from as low as 220 degrees Fahrenheit going up to 395 degrees. Take a thin section of hair alongside your face – ½ to 1 inch wide -- and clip it off to the side with the top layer of untreated hair. It will remain untreated and smooth. 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.