Prediction of 2D Membrane semantics/instances

This guide provides detailed instructions to perform fully automatic membrane segmentation on all of your micrographs using our most up-to-date model.

TARDIS can predict fully automatic membranes as semantic labels, or instances [track, labels, point cloud].

../_images/2d_mem.jpg

Data source: Dr. Victor Kostyuchenko and Prof. Dr. Shee-Mei Lok, DUKE-NUS Medical School Singapore

Example of segmented micrograph with indicated predicted semantic binary segmentation and individual instances represented as tracks of different colors.

TARDIS Workflow

  1. Prepare a folder with data.

  2. Predict membrane segmentation

  3. (Optional) Advance prediction setting

Preparation

Simply store all your micrographs in one folder. TARDIS will recognize all image file with the extension [.tif, .tiff, .rec, .map, .mrc, .am, .npy].

Tip: In the case of REC/MAP/MRC files try to make sure that files have embedded in the header pixel size information. You can check it with Imod header in your bash terminal.

Prediction

(Optional) Type the following to check if TARDIS is working properly.

Tips: If any error occurs, try using our troubleshooting chapter.

tardis

This will display the TARDIS interface and show available options.

../_images/main_tardis.jpg

Semantic/Instance segmentation:

For the semantic prediction, you only need to type:

tardis_mem2d -dir <path-to-your-micrographs> -out <output_type>

TARDIS will save predictions in the default folder Prediction located in the folder with your data.

Running this will segment all micrographs in the indicated path. Predicted output will be store in file format indicated in -out <output_type> [see all -out options].

You can also segment individual file by replacing -dir with file not a folder location.

For example:

tardis_mem2d -dir <path-to-your-micrographs> -out mrc_None

Will perform only semantic segmentation and save the output file as a .mrc file.

tardis_mem2d -dir <path-to-your-micrographs> -out None_csv

Will perform only instance segmentation and save the output file as a .csv file with data structure as [Membrane ID x X x Y]

tardis_mem2d -dir <path-to-your-micrographs> -out mrc_csv

Will perform semantic and instance segmentation and save the output file as a .mrc and a .csv files.

Advance usage:

Below you can find all available arguments you can use with tardis_mem2d, with the explanation for their functionality:

-dir or --path: Directory path with all micrographs for TARDIS prediction.

  • default: Current command line directory.

-ms or --mask: Define if your input is a binary mask with a pre-segmented membrane.

  • Example: You can set this argument to -ms True if you have already segmented membrane and you only want to segment instances.

  • default: False

  • Allowed options: True, False

-px or --correct_px: Overwrite pixel value.

  • Example: You can set this argument to -px True if you want to overwrite the pixel size value that is being recognized by TARDIS.

  • default: False

  • Allowed options: True, False

-ch or --checkpoint: Directories to pre-train models.

  • Example: If you fine-tuned TARDIS on your data you can indicate here file directories for semantic and instance model. To do this type your directory as follow: -ch <semantic-model-directory>|<instance-model-directory>. For example, if you want to pass only semantic model type: -ch <semantic-model-directory>|None.

  • default: None|None

-out or --output_format: Type of output files.

  • Example: Output format argument is compose of two elements -out <format>_<format>. The first output format is the semantic mask, which can be of type: None [no output], am [Amira], mrc, or tif. The second output is predicted instances of detected objects, which can be of type: output as amSG [Amira spatial graph], mrc [mrc instance mask], tif [tif instance mask], csv coordinate file [ID, X, Y], stl [mesh grid], or None [no instance prediction].

  • default: mrc_csv

  • Allowed options: am_None, mrc_None, tif_None, npy_None, None_am, am_am, mrc_am, tif_am, npy_am, None_amSG, am_amSG, mrc_amSG, tif_amSG, npy_amSG, None_mrc, am_mrc, mrc_mrc, tif_mrc, npy_csv, None_tif, am_tif, mrc_tif, tif_tif, npy_tif, None_csv, am_csv, mrc_csv, tif_csv, npy_csv, None_stl, am_stl, mrc_stl, tif_stl, npy_stl, None_npy, am_npy, mrc_npy, tif_npy, npy_npy,

-ps or --patch_size: Window size used for prediction.

  • Example: This will break the micrograph into smaller patches with 25% overlap. Smaller values than 256 consume less GPU, but also may lead to worse segmentation results!

  • default: 256

  • Allowed options: 32, 64, 96, 128, 256, 512

-rt or --rotate: Predict the image 4 times rotating it each time by 90 degrees.

  • Example: If -rt True, during semantic prediction micrographs is rotate 4x by 90 degrees. This will increase prediction time 4 times. However, it usually will result in cleaner output.

  • default: True

  • Allowed options: True, False

-ct or --cnn_threshold: Threshold used for semantic prediction.

  • Example: Higher value then -ct 0.5 will lead to a reduction in noise and membrane prediction recall. A lower value will increase membrane prediction recall but may lead to increased noise.

  • default: 0.5

  • Allowed options: Float value between 0.0 and 1.0

-dt or --dist_threshold: Threshold used for instance prediction.

  • Example: Higher value then -dt 0.5 will lower number of the predicted instances, a lower value will increase the number of predicted instances.

  • default: 0.5

  • Allowed options: Float value between 0.0 and 1.0

-pv or --points_in_patch: Window size used for instance prediction.
  • Example: This value indicates the maximum number of points that could be

    found in each point cloud cropped view. Essentially, this will lead to dividing a point cloud into smaller overlapping areas that would be segmented individually and then stitched and predicted together. Tips: 1000 points per crop requires ~12 GB of GPU memory. For GPUs with smaller amounts of GPU memory, you can use lower numbers 500 or 800. A higher number will always lead to faster inference, and may slightly improve segmentation.

  • default: 1000

  • Allowed options: Int value between 250 and 5000.

-cc or --connect_cylinder: Cylinder radius used to filter unconnected components.

  • Example: To minimize false positives when linking membranes,

we limit the search area to a cylindrical radius specified in angstroms. For each spline, we find the direction the filament end is pointing in and look for another filament that is oriented in the same direction. The ends of these filaments must be located within this cylinder to be considered connected.

  • default: 40

  • Allowed options: Float value between 0 - inf

-cm or --connect_membranes: Cylinder radius used to filter unconnected components.

  • Example: To address the issue where membrane are mistakenly

identified as two different filaments, we use a filtering technique. This involves identifying the direction each membranes end points and then linking any membranes that are facing the same direction and are within a certain distance from each other, measured in angstroms. This distance threshold determines how far apart two membranes can be, while still being considered as a single unit if they are oriented in the same direction.

  • default: 1000

  • Allowed options: Float value between 0 - inf

-dv or --device: Define which device to use for inference.

  • Example: You can use -dv gpu to use the first available gpu on your system. You can also specify the exact GPU device with the number -dv 0, -dv 1, etc. where 0 is always the default GPU. You can also use -dv cpu to perform inference only on the CPU.

  • default: 0

  • Allowed options: cpu, gpu, 0, 1, 2, 3, etc.

-db or --debug: Enable debugging mode.

  • Example: Debugging mode saves all intermediate files allowing for debugging any errors. Use only as a developer or if specifically asked for by the developer.

  • default: False

  • Allowed options: True, False