[Insight-users] Estimation of abdominal volume

[Luis Ibanez]

Hi Frederic,


1) Why did you decided to use CT for this measurement ?

    There are certain MRI protocols that will highlight
    fat and therefore help to mark the peritoneum...

    This may simplify your image processing task, not to
    mention avoid a significant amount of ionizing radiation
    to your patient.



    To quote Dr. Michael Vannier

        "Good imaging beats good image processing"



     When segmentation becomes too difficult, you should
     take that as an indication that the image modality,
     and/or the imaging protocol are not well suited for
     this task.



2) It seems to be reasonable to expect that the volume
    of the abdominal cavity will be extremely variable during
    the day.  For a longitudinal analysis to make sense you
    have to solve the very challenging task of establishing
    conditions where the images acquired at day #1 can be
    considered similar to those of day #N.  Those daily
    variations are probably larger than the actual accuracy
    of a segmentation method.



3) This problem seems to be a good candidate for an atlas
    based registration approach.  In this context you could
    do the following:

    3.1) Invest a large amount of effort in obtaining a
         segmentation of the abdominal cavity from an
         initial image of the patient. This may involve
         a mixture of supervised segmentation algorithms
         and a retouching/evaluation stage.

    3.2) Once you have segmented the initial dataset, you
         could perform deformable registration between this
         first original dataset and the new ones, and use
         the resulting deformation field for mapping the
         initial segmentation. In this way, the deformation
         field itself will tell you the changes on the volume
         in the cavity. In fact, by computing the Jacobian
         of the deformation field you can estimate local
         expansions and contractions. (at least in theory)   :)




Regards,


     Luis



--------------------------
Frederic Perez wrote:
> Dear Insight-users,
> 
> being stalled at the moment, I thought I better ask you for hints or 
> advice. Any help will be greatly appreciated. Our problem follows:
> 
> [Input]
> We have a set of 3D CT images, obtained using the same scanner, from
> a set of patients ($i = 1..N$). For each patient $i$ two images $I$ 
> are derived, taken at different days, to assess the response to 
> therapy: $I(i, t_0)$ and $I(i, t_1)$.
> 
> [Target]
> The objective is to measure the difference, per patient, of the 
> abdominal volume ($V_a(I(i, t_1)) - V_a(I(i, t_0))$).
> 
> [How-to?]
> I guess that the segmentation of the true abdominal cavity is a very
> difficult problem (small narrowness of the peritoneum, i.e. of the 
> membrane lining the abdominal cavity, the hole due to the esophagus,
> etc.). Hopefully, however, an approximation will suffice.
> So far, for each image we have segmented most of the trunk by means 
> of a  pipeline of filters (ExtractImageFilter, MaskImageFilter, etc.):
> $T(I(i, t_0))$ and $T(I(i, t_1))$.
> We can compute volumes with a ImageMomentsCalculator object, but only
> when provided with consistent input we will obtain meaningful results.
> This means that we must crop each pair $T(I(i, t_0))$ and
> $T(I(i, t_1))$ in such a way that the limiting planes are tightly 
> related. The question is, how do we define those limiting planes?
> We thought about using anatomical landmarks, automatically defined,
> but do not how to do this (maybe with landmarks on the bones?---which
> ones?). Is this the right direction to pursue?
> 
> May thanks in advance,
> 
> Frederic Perez
> 
> 
> 
>