I would like to make a follow-up to this discussion:

The data I supplied above have the problem that the "wires" in each slice are shifted relative to each other. The following image should make clear what I am talking about:

This of course is a problem when building a regular mesh. I am trying a correction by shifting the wires in a way that point distances within neighboring slices become minimal like so:

Our complete code now reads:

ClearAll["Global`*"]
SetDirectory[NotebookDirectory[]];
sliceData = << "SliceData.txt";

bsp = BSplineFunction[#, SplineClosed -> False] & /@ sliceData;

highResData = Table[bsp[[h]][t], {h, 1, Length[bsp]}, {t, 0, 1, .003}];

(* definition of difference between to sets of "wire data" - to be minimized: *)    
totalPointDist[data1_List, data2_List, n_?NumericQ] := 
 Total[EuclideanDistance[#1, #2] & @@@ Transpose[{data1, RotateLeft[data2, n]}]]

(* minimization and shift: *)
Monitor[
 Do[highResData[[n]] = 
   RotateLeft[highResData[[n]], \[FormalN] /. Last@Minimize[totalPointDist[highResData[[n - 1]], 
        highResData[[n]], \[FormalN]], \[FormalN], Integers]], {n, 2, Length[highResData]}], n]

So with the friendly help of @Marco Thiel, @Vitaliy Kaurov, @Chip Hurst one ends up with:

I.e.now there appears to be no more mesh defects! The effect can also be seen by showing the vertical lines only (according to Vitaliy):

Congratulations @Henrik Schachner! Congratulations all the contributors! Impressive common result!

There are impressive both the result and the Community contribution. So, I think your new posts may have similar resonance.

Dear @Vitaliy Kaurov, dear @Chip Hurst,

thank you very much for your valuable contributions - I learned a lot! And I think BSplineFunction does the trick. (Image3D is less suitable because of distortions.)

The data stem from our radiotherapy treatment planning system. There we are working with contours which are drawn on any single CT slice. Organs at risk and target volumes are defined by those contours. One special contour is the "BODY contour"; this is what is shown in my example data. Dose will be calculated only inside this BODY contour, therefore e.g. the volume around the ears and nose appears to be exaggerated (for being on the safe side). Those treatment plans can be exported as DICOM files and nicely imported in Mathematica.

When high energy radiation is applied to a body it turns out that the dose next to the skin is highly diminished; this is due to the buildup effect of the dose. When full dose at the skin is wanted, one has to anticipate this buildup effect, and this can be realized by putting a "flab" onto the skin: A flab is a layer made of some tissue equivalent material.

Sorry for this lengthy explanation! My point is: Optimal flabs can have quite irregular shapes. I recently learned at a conference that there is the possibility to 3D print those individual flabs - if one can provide the data ... So - probably to everybody's disappointment - I do not want to print any BODY contour, but I imagined a BODY contour might serve here as kind of a "honey pot".

Best regards and many thanks! -- Henrik

In addition to @Marco Thiel nice suggestions, I'd like to mention a possibility of resampling and building a spline surface. You have nice dense data. But number of points in each loop is different. A high resolution resampled 3D array of 3D points where number of points in each loop is the same could be your final output object. This could help 3D printing too. We start from creating a spline curve in 3D for every loop:

loopsBSF=BSplineFunction/@sliceData;
ParametricPlot3D[#[t]&/@loopsBSF,{t,0,1},
    PlotTheme->"Detailed",PlotLegends->None,PlotStyle->Thickness[0]]

where the loops are already parametric spline functions and not data points. Now we build a higher resolution array with 1000 points per every loop and a spline surface:

higHreSdatA=Table[loopsBSF[[k]][t],{k,Length[sliceData]},{t,0,1,.001}];
surfBSF=BSplineFunction[higHreSdatA,SplineClosed->{False,True}]

ParametricPlot3D[surfBSF[u,v],{u,0,1},{v,0,1},
    PlotRange->All,Mesh->All,PlotStyle->{Orange,Specularity[White,10]},
    SphericalRegion->True,PlotTheme->"Detailed",PlotLegends->None]

You can increase resampling generally for a higher resolution. You can also ask for surface thickness and make .STL file for 3D priniting:

ParametricPlot3D[surfBSF[u,v],{u,0,1},{v,0,1},
    PlotRange->All,Mesh->All,PlotStyle->{Orange,Specularity[White,10]},
    SphericalRegion->True,PlotTheme->"ThickSurface",PlotLegends->None]
Import[Export["test.stl",%]]

And this is object build now from only vertical lines - the new information derived from your data:

Graphics3D[{Opacity[.2], Line[Transpose[higHreSdatA]]}, Boxed -> False, SphericalRegion -> True]

Awesome tip, @Chip Hurst! RepairMesh is indeed a nice function to take advantage of. Repairing defects in the mesh regions is a crucial step for 3D printing.

It's towards the bottom of the Details and Options section in the documentation for MeshRegion and BoundaryMeshRegion.

Dear Marco,

thank you very much for taking the time looking into this problem!

Actually ListSurfacePlot3D in this case would be the perfect function - which I missed completely (shame on me!). It is a pity that it shows those artifacts. I imagine that someone from WR knows some undocumented Method options ...

Otherwise your second solution seems to show a way to go. Probably I have to modify it a bit, because it can happen that there are several disjunct areas in one slice. My final goal here is to end up with something which can be exported for 3D printing.

Again many thanks and best regards from Germany! -- Henrik

Nice idea to use Image3D. As of version 11, one can smooth things out a bit with ImageMesh:

(* imgs is defined from the above post *)
im = ImageRotate[ColorNegate[Image3D[imgs]], {?, {0, 1, 0}}];

(* reconstruct data with marching cubes *)
mesh = ImageMesh[im, Method -> "DualMarchingCubes"];

(* visualize with nice box ratios *)
BoundaryMeshRegion[mesh, BoxRatios -> {9, 6, 7}]

For visualization purposes only, we can smooth things out a bit more with PlotTheme -> "SmoothShading":

BoundaryMeshRegion[mesh, BoxRatios -> {9, 6, 7}, PlotTheme -> "SmoothShading"]