lesion_sci_t2

Intramedullary SCI lesion and cord segmentation in T2w MRI

This segmentation model for spinal cord injury segmentation uses a 3D U-Net architecture, and was trained with the nnUNetV2 framework. It is a multiclass model, outputting segmentations for both the hyperintense SCI lesions and spinal cord. Training data consisted of T2w images from 7 sites with traumatic (acute pre-operative, intermediate, chronic), non-traumatic (DCM) and ischemic SCI lesions spanning numerous resolutions, orientations, as well as multiple scanner manufacturers and field strengths.

Reference

@InProceedings{10.1007/978-3-031-82007-6_19,
               author="Karthik, Enamundram Naga and Valo{{s}}ek, Jan and Farner, Lynn and Pfyffer, Dario and Schading-Sassenhausen, Simon and Lebret, Anna and David, Gergely and Smith, Andrew C. and Weber II, Kenneth A. and Seif, Maryam and Freund, Patrick and Cohen-Adad, Julien",
               editor="Wu, Shandong and Shabestari, Behrouz and Xing, Lei",
               title="SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury",
               booktitle="Applications of Medical Artificial Intelligence",
               year="2025",
               publisher="Springer Nature Switzerland",
               address="Cham",
               pages="198--209",
               abstract="Spinal cord injury (SCI) is a devastating incidence leading to permanent paralysis and loss of sensory-motor functions potentially resulting in the formation of lesions within the spinal cord. Imaging biomarkers obtained from magnetic resonance imaging (MRI) scans can predict the functional recovery of individuals with SCI and help choose the optimal treatment strategy. Currently, most studies employ manual quantification of these MRI-derived biomarkers, which is a subjective and tedious task. In this work, we propose (i) a universal tool for the automatic segmentation of intramedullary SCI lesions, dubbed SCIsegV2, and (ii) a method to automatically compute the width of the tissue bridges from the segmented lesion. Tissue bridges represent the spared spinal tissue adjacent to the lesion, which is associated with functional recovery in SCI patients. The tool was trained and validated on a heterogeneous dataset from 7 sites comprising patients from different SCI phases (acute, sub-acute, and chronic) and etiologies (traumatic SCI, ischemic SCI, and degenerative cervical myelopathy). Tissue bridges quantified automatically did not significantly differ from those computed manually, suggesting that the proposed automatic tool can be used to derive relevant MRI biomarkers. SCIsegV2 and the automatic tissue bridges computation are open-source and available in Spinal Cord Toolbox (v6.4 and above) via the sct{\_}deepseg -task seg{\_}sc{\_}lesion{\_}t2w{\_}sci and sct{\_}analyze{\_}lesion functions, respectively.",
               isbn="978-3-031-82007-6"
}

Project URL: https://github.com/ivadomed/model_seg_sci

usage: sct_deepseg lesion_sci_t2 [-i <file> [<file> ...]] [-o <str>] [-install]
                                 [-custom-url CUSTOM_URL [CUSTOM_URL ...]]
                                 [-thr <float>] [-largest {0,1}]
                                 [-fill-holes {0,1}]
                                 [-remove-small REMOVE_SMALL [REMOVE_SMALL ...]]
                                 [-qc <folder>] [-qc-dataset <str>]
                                 [-qc-subject <str>] [-qc-plane <str>]
                                 [-qc-seg <file>] [-h] [-v <int>]
                                 [-profile-time [<file>]]
                                 [-trace-memory [<folder>]] [-r {0,1}]

INPUT/OUTPUT

-i

Image to segment. Can be multiple images (separated with space).

Note: If choosing lesion_ms_mp2rage, then the input data must be cropped around the spinal cord. (To crop the data you can first segment the spinal cord using the contrast agnostic model. This could be done using: “sct_deepseg spinalcord -i IMAGE -o IMAGE_sc”, then crop the image with 30 mm of dilation on axial orientation around the spinal cord. This could be done using: “sct_crop_image -i IMAGE -m IMAGE_sc -dilate 30x30x5”. Note that 30 is only for 1mm isotropic resolution, for images with another resolution divide 30/your_axial_resolution.)

-o

Output file name. In case of multi-class segmentation, class-specific suffixes will be added. By default,the suffix specified in the packaged model will be added and output extension will be .nii.gz.

TASKS

-install

Install models that are required for specified task.

Default: False

-custom-url

URL(s) pointing to the .zip asset for a model release. This option can be used with -install to install a specific version of a model. To use this option, navigate to the ‘Releases’ page of the model, find release you wish to install, and right-click + copy the URL of the .zip listed under ‘Assets’. NB: For multi-model tasks, provide multiple URLs. For single models, just provide one URL. Example: sct_deepseg rootlets -install -custom-url https://github.com/ivadomed/model-spinal-rootlets/releases/download/r20240523/model-spinal-rootlets_ventral_D106_r20240523.zip sct_deepseg rootlets -i sub-amu01_T2w.nii.gz

PARAMETERS

-thr

Binarize segmentation with specified threshold. Set to 0 for no thresholding (i.e., soft segmentation). Default value is model-specific and was set during optimization (more info at https://github.com/sct-pipeline/deepseg-threshold).

-largest

Possible choices: 0, 1

Keep the largest connected object from each output segmentation; if not set, all objects are kept.

Default: 0

-fill-holes

Possible choices: 0, 1

If set, small holes in the segmentation will be filled in automatically.

Default: 0

-remove-small

Minimal object size to keep with unit (mm3 or vox). A single value can be provided or one value per prediction class. Single value example: 1mm3, 5vox. Multiple values example: 10 20 10vox (remove objects smaller than 10 voxels for class 1 and 3, and smaller than 20 voxels for class 2).

MISC ARGUMENTS

-qc

The path where the quality control generated content will be saved.

-qc-dataset

If provided, this string will be mentioned in the QC report as the dataset the process was run on.

-qc-subject

If provided, this string will be mentioned in the QC report as the subject the process was run on.

-qc-plane

Possible choices: Axial, Sagittal

Plane of the output QC. If Sagittal, it is highly recommended to provide the -qc-seg option, as it will ensure the output QC is cropped to a reasonable field of view. (Note: Sagittal view is not currently supported for rootlets/totalspineseg QC.)

Default: “Axial”

-qc-seg

Segmentation file to use for cropping the QC. This option is useful when you want to QC a region that is different from the output segmentation. For example, for lesion segmentation, it might be useful to provide a cord segmentation to expand the QC field of view to include the full cord, while also still excluding irrelevant tissue. If not provided, the default behavior will depend on the -qc-plane:

• ‘Axial’: A sensibly chosen crop radius between 15-40 vox, depending on the resolution and segmentation type.

• ‘Sagittal’: The full image. (For very large images, this may cause a crash, so using -qc-seg is highly recommended.)

-v

Possible choices: 0, 1, 2

Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode.

Default: 1

-profile-time

Enables time-based profiling of the program, dumping the results to the specified file.

If no file is specified, human-readable results are placed into a ‘time_profiling_results.txt’ document in the current directory (’/home/docs/checkouts/readthedocs.org/user_builds/spinalcordtoolbox/checkouts/stable/documentation/source’). If the specified file is a .prof file, the file will instead be in binary format, ready for use with common post-profiler utilities (such as snakeviz).

-trace-memory

Enables memory tracing of the program.

When active, a measure of the peak memory (in KiB) will be output to the file peak_memory.txt. Optionally, developers can also modify the SCT code to add additional snapshot_memory() calls. These calls will ‘snapshot’ the memory usage at that moment, saving the memory trace at that point into a second file (memory_snapshots.txt).

By default, both outputs will be placed in the current directory (’/home/docs/checkouts/readthedocs.org/user_builds/spinalcordtoolbox/checkouts/stable/documentation/source’). Optionally, you may provide an alternative directory (-trace-memory <dir_name>), in which case all files will be placed in that directory instead. Note that this WILL incur an overhead to runtime, so it is generally advised that you do not run this in conjunction with the time profiler or in time-sensitive contexts.

-r

Possible choices: 0, 1

Remove temporary files.

Default: 1