Amira 3d pdf

Samples of 25 and 21 sections were used from the E The morphology experts all have several years of experience in research in morphogenesis and heart development. In this test, the experts could use a simple user interface to interactively find the position of an input section in the 3D reference model. A randomly presented sample of 24 sections was used.

This set of images was drawn from the set of cross-sections of the episcopic test model that was used for the episcopic-episcopic test of the program. In all performance tests the reference model of an E Test images were generated as cross-sections of the aligned 3D test models.

In this way, the approximate position of each cross-section of the test model is known in the reference model, which enables the computation of a fit error. This fit error takes the distance and the angle between the determined and actual cross-section into account and is normalized to values between 0 and see Appendix S2 in the supplementary material.

Box plots show the results of the performance tests. The red line denotes the median fit error and the blue box shows the interquartile range; outliers indicated by plus signs are defined as results more than 1. Because of the non-normal distribution of the normalized fit error, nonparametric statistics were used to compare the results of the different performance tests.

To this end, the Kruskal-Wallis test n groups or the Mann-Whitney test two groups was used Conover, The full protocol for generating a basic and an advanced 3D pdf is given in the Appendix S1 in the supplementary material.

The protocol assumes that the user is already familiar with creating 3D reconstructions from histological sections Soufan et al.

The basic protocol is used to generate a simple 3D pdf of a reconstructed 3D object and is customized for the conversion from Amira to Acrobat. The resulting 3D pdf can be enhanced to generate an advanced 3D pdf that can be used more easily and intuitively. These independent enhancements can be tailored to the document or paper at hand. By adding annotations and preset views, the author can better convey the insights obtained from the study of the 3D object.

Also, functionality and robustness can be added by applying scripts to buttons and the document itself. This advanced protocol is independent of the original 3D reconstruction program. In the input section, the myocardium needs to be separated from background staining using an intensity threshold or manual segmentation. Using the supplied user interface Fig. A similarity metric based on the contour of cardiac tissue is used to select the most similar cross-section of the reference model.

To avoid repetitive computations, a database was created containing pre-computed cross-sections of the reference model Fig. To reduce misplacement and processing time, basic image features such as section size, tissue density, center of gravity and regional density are used to preselect the cross-sections from the reference model that are to be compared with the input image de Boer et al. Details of the similarity metric, the reference model database and the image features are given in Appendix S2 in the supplementary material.

In the import dialog top , the user can select the stage into which the input section has to be fitted. After import of the image the user can define a region of interest in which an automatic intensity threshold is employed for segmentation of the myocardium. Next, islands of an adjustable number of pixels can be automatically removed middle. The myocardium is then mapped into the reference model of the selected age. The result window bottom shows the input image with annotated cardiac compartments and the position of the plane in the reference model.

The latter view can be interactively viewed from different directions. The test was performed for six different feature settings of TRACTS: purely brute force; only constraint on size; the combinations of size with center of mass, density or regional density; and all features combined. The normalized fit error was computed for each section in each test Fig. Computation time per image is given in Fig.

Performance was significantly improved when only cross-sections that are similar in size were compared. The second largest effect was observed with the regional density feature. Using additional features individually gave only a small further improvement, but each feature did result in a reduction in the time needed to register an input section.

The combination of all features did not further reduce the fit error but led to a further reduction of the computation time to only 30 seconds per section. Some of the sections that were placed at an entirely incorrect position when using only the size constraint were fitted near the correct position when preselection on image features was applied Fig. The center of mass feature rejected a section cut through a relatively symmetrical part of the heart; because the dorsal wall of each ventricle is thicker than the ventral wall, the mirrored section that was first found was rejected Fig.

The overall tissue density feature served to reject a misplaced section where the high tissue density of the erroneously chosen left ventricle was replaced by the correct right atrium Fig.

The regional density feature showed its effectiveness by rejecting a solution in which a right atrium was placed on a section through the left ventricular wall Fig. Effect of image features and interpretation of the fit error. A Added image features reduce misplacements. The middle row shows which reference sections were found using only a selection on heart size. The bottom row shows the reference section recovered when an additional feature is used.

The normalized fit error Fe is given for each of the mis placed images. OFT, outflow tract. B Comparison of the fit errors with their corresponding planes based on the first lane of A. The blue planes are the input section and the red planes are the positions found by TRACTS, using the size constraint top or the center of mass constraint bottom.

C Examples to help with the interpretation of the normalized fit error. Lanes 1 and 2 show the translation and rotation resulting in a fit error of 1.

Lanes 3, 4 and 5 show larger fit errors, although in all cases the correct compartments are recovered. The fit errors are The latter fit error is close to the 75th percentile median, 1. In column one, a parallel section is shown with a fit error close to the 75th percentile. In the second column, a section fitted with a similar error is shown, in which the error is mainly due to the angle between the planes.

Columns 3 through 5 show significantly higher fit errors, although in each case the correct anatomical compartments are found. Despite the deformations resulting from paraffin embedding, sectioning and mounting, TRACTS was able to correctly place most paraffin sections into a reference model Fig. The two paraffin models in this test were from mouse embryos of stage E A panel of cardiac embryologists was asked to manually register a subset of the images used in the above episcopic-episcopic test in the 3D reference model.

They could place a plane in a 3D surface reconstruction by changing its position, tilting angle and tilting direction. The interface showed the cross-section at the position of this interactive plane. The box plots Fig. Obviously, sections showing only a single atrium or ventricle can seldom be fitted to their correct position.

Moreover, the left-right symmetry of the heart can lead to cross-sections through both the atrium and the ventricle and no other structures. Such sections can be fitted equally well on the left or right side of the heart. Finally, rotational symmetry along an axis through the atrium and ventricle can lead to sections showing the correct atrium and ventricle lumen without further clues as to the sectioning direction Fig.

In these cases, the annotation of the anatomical compartments will be correct. Some of the high fit errors in TRACTS performance could have resulted from biological variation between the reference and test models. Sections fitted in the anatomically right position will then result in a relatively high fit error because the structures are positioned slightly differently in the reference and test models. To disseminate 3D information, a surface-rendered 3D object can be converted into a 3D pdf using Acrobat and subsequently viewed, and interacted with, using Adobe Reader see Fig.

S3 in the supplementary material. The object should be a 3D surface, stored by Amira as an Amira Surface file. The object should not be too complicated i. The basic protocol describes how to configure Amira and Acrobat and to properly export the 3D object into a pdf via a single command. The captured pdf can be saved as a basic 3D pdf and the embedded 3D object can be handled interactively using the default Adobe Reader user interface.

Unfortunately, direct capture of an object that consists of several structures will result in an unusable object in Acrobat owing to the separately colored inner and outer surfaces of each structure; juxtaposed structures lead to even more surfaces that are wrongly colored, separated or shared in Acrobat. To solve this, a separate surface for each structure of the object has to be created and assigned the same inside and outside color.

Because this can be very laborious, we wrote an Amira script that does this via a single command see the scripts file in the supplementary material. When the surfaces are thus assigned, creating a 3D pdf from an object displayed in the Amira Viewer window becomes as simple as pressing the Print Screen button. A capture to Acrobat then contains all separate structures of the object colored correctly.

The names assigned to the structures are lost by the capture procedure and reappear as numbers. These can be renamed in Acrobat. Win64 1CD Oasys Compos v8. Win64 1CD Oasys. Win64 1CD Oasys Pdisp v Win64 1CD Oasys Pile v Linux64 2CD Oasys Suite v Solaris Oasys.

SR8 1CD Geocentrix. Axpile v3. Trench v5. Win64 1CD Rocscience. Mpile 4. Only 1CD Delft. Msheet 7. Chasm Consulting Ventsim Visual Premium. Gemcom GEMS v6. Gemcom Minex. Gemcom Minex v6. Win64 1CD Dassault. Manual 1CD Quarry v6. Win32 1CD Tadpro. Solutions GSS. Presgraf v Linux 1CD Axon. X64 1DVD Genesis v1. Linux 1CD Safe. Linux 1CD Fabric. X64 1CD Fabric. Retail 1CD Eclipse Platform v3. Linux 1CD Understand. Solaris 1CD Understand. Solaris 1CD Understand for Fortran v1.

Net 3. Linux64 2CD Tecplot. Linux64 1CD Tecplot. Win64 1CD Tecplot. WinAll 1CD Exceed. Linux64 3CD Maplesoft Maple v These examples, presumably representing the first such models published, are developmental stages of an evertebrate Patella caerulea, Mollusca and a vertebrate species Psetta maxima, Teleostei obtained from histological section series reconstruction processed with the software package Amira. These surface rendering models are particularly suitable for a PDF file because they can easily be transformed to a file format required and components may be conveniently combined and hierarchically arranged.

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