Tutorial

Biomedisa bases on a semi-automatic segmentation algorithm. That means, before you perform the segmentation process, you must pre-segment some reference slices in any segmentation editor of your choice. The information given by these slices is then used to automatically segment the remaining volume. To start the segmentation process, upload your image data and your pre-segmented slices as Multipage-TIFF or as an Amira file, click the Start button, and download or visualize your final segmentation.

To segment the image data, several weighted random walks start in the pre-segmented reference slices and walk through the volume. Over time, the voxels are hit by numerous random walks. Based on the respective number of hits, it is possible to determine the probability that a voxel belongs to one particular segment. The segmentation can thus be performed by assigning each voxel to the region from which most of the hits originate. You can imagine Biomedisa as an interpolation taking into account the gray values. But your pre-segmented slices are also recalculated.

Your image and label data can be either a 3D Multipage-TIFF file, an Amira file or a zipped folder containing DICOM images. Popular analytic tools like Amira or Fiji/ImageJ can save your data as a TIFF file. The data type can be 8-bit, 16-bit or 32-bit integer or float. You can even upload 64-bit images but they are automatically converted to 32-bit during the calculation.

The label file contains pre-segmented reference slices. It can be either a 3D Multipage-TIFF file, an Amira file or a zipped folder containing DICOM images. It must have exactly the same xyz-dimensions like the uploaded image file. You can save it either as a gray-value or as a color image. In both cases, each gray value or color corresponds to one segment. If you use an Amira label file, all your information, like names etc., will be preserved so that post-processing is easy after importing to Amira again. For example, if you have two segments, you could use 50 for the first and 100 for the second. The background must always be 0. The background is alway considered as an additional segment. Attention! Once you label a slice, all segments existing in this slice must be labeled. Otherwise, these areas are mistakenly considered as background, and your segmentation will not be correct.

Yes, it is possible to label in different planes. To enable this, please click on the settings icon next to your label file and activate this feature. You can then label in all three planes simultaneously or only in one or two planes of your choice. Attention! There must be at least one empty slice between your pre-segmented slices. The algorithm needs this to detect in which planes you have labeled slices.

Unfortunately, it is not possible to answer this question in general. It depends on several factors. But Biomedisa checks whether your data can be processed or not. If your data is too large, it will automatically complain and stop the process. To give you an idea of how big your data can be. Depending on your labels, Biomedisa will automatically crop your image to a proper region of interest. This region of interest can be roughly 8 GB large (e.g. 2000 x 2000 x 2000 for 8bit images). So even if your volume is much larger, it can be processed as long as your region of interest fits into that "box". It also depends on the number of reference slices you have pre-segmented. The more slices were labeled, the smaller the region of interest can be.

Sometimes it is useful to ignore a specific area. Therefore, you can specify a label that should be ignored. No random walks will start in this label field. Note that this differs from "nothing is labeled" because that means that the area belongs to the background, thus starting random walks for the background.

In addition to the regular diffusion result, you can choose between cleaned (removes outliers), filled holes, postprocessed with active contours, uncertainty (a probability map how certain your result is), and a smoothed version. You can fine-tune these features in the settings .

Yes, you can share your data. You can either share it with one or multiple users by entering their user names or you create a password protected download link .

You can use the move icon to move your data between your projects or from and to your storage.

Yes, you can have a quick look at your data with the slice viewer or visualize it with our 3D rendering software by clicking on the file icon .

You can train a neural network by selecting all image files you want to consider and the corresponding already fully segmented label files. Then press the AI button . After the training process is completed, you can select the trained network and one or more images for which you want to predict the segmentation. Finally press the predict button .

Biomedisa wants to speed up and improve your daily work. Instead of manually labeling each slice of your volume, Biomedisa uses only a few pre-segmented slices to segment the rest of your data. But it is not a model-based segmentation method. That means, that if you do not see any structures in your data or you can not see the structures are separated, then Biomedisa is probably not able to see it either.

Biomedisa will soon be an open source project. Until then you can use it for free online by creating an account (sign in). If you use Biomedisa results in your publications please cite the following paper:
Lösel, P. and Heuveline, V. (2016): Enhancing a diffusion algorithm for 4D image segmentation using local information, Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842L, doi: 10.1117/12.2216202.

Lösel, P. and Heuveline, V. (2016): Enhancing a diffusion algorithm for 4D image segmentation using local information, Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842L, doi: 10.1117/12.2216202.