myoL3

Automated L3-level trunk-muscle analysis from CT. The tool takes a full-body abdominal CT, it resamples to the working resolution, localizes the L3 level and crops to it, then runs muscle segmentation on the crop.

full-body CT (.nii.gz)
      │  resample to target spacing
      ▼
  L3 localizer (presence-gated sliding window)  ──►  z-crop
      ▼
  muscle segmentation (nnU-Net, in-Python)  ──►  raw label map
      ▼
  strip interior fat (intramuscular adipose tissue)  ──►  TOTAL muscle segmentation (final)

The muscle segmenter downloads weights from HuggingFace and runs inference on the full CT (Computed Tomography) first to get L3 bounds. The segmentation model is then run on the cropped L3-bounded image to label the muscles. The fat step removes solidly-fat interior voxels (< −30 Hounsfield units, HU, inside the eroded muscle core) from each muscle.

Example (L3 axial slice)

Input CT	Muscle segmentation	Body-composition map

• Muscle segmentation — psoas (red), quadratus lumborum (green), erector spinae / multifidus (blue).

• Body-composition map — each muscle is split into four compartments: muscle, low-attenuation ("fat partial-volume") muscle, intramuscular fat, and the partial-volume boundary rim. The muscle / low-attenuation split is a per-scan, per-muscle Gaussian Mixture Model (GMM), interior fat (< −30 HU) is removed to form the total-muscle segmentation.

  Gaussian Mixture Model (GMM) — a muscle's HU values form two overlapping populations, denser normal muscle and lower-HU fatty muscle. The GMM fits one bell curve to each and puts the split where they cross, so the threshold adapts to each patient's muscle attenuation instead of a fixed cutoff (a myosteatotic patient's split lands much lower).

Installation

Install PyTorch first (pytorch.org), then:

pip install myoL3

Or clone and install editable for development:

git clone https://github.com/Manskelab/myoL3 myoL3
cd myoL3
pip install -e .

Model weights

Both checkpoints live in one Hugging Face repo (YousifKhoury/myoL3): l3_localizer.pt (L3 bounds localizer) and muscle_seg.pth (UNet muscle segmenter). Download them once:

myol3-install /path/to/models        # downloads both + remembers the paths

or point at local files without downloading:

export MYOL3_LOCALIZER_CKPT=/path/to/l3_localizer.pt
export MYOL3_SEGMENTER_CKPT=/path/to/muscle_seg.pth

Resolution order per model: --*-ckpt flag → env var → myol3-install path (~/.myol3/config.json) → Hugging Face download.

Models

• L3 localizer (l3_localizer.pt) — a Residual Network (ResNet-18) encodes each axial slice, a bidirectional Gated Recurrent Unit (GRU) runs over the superior–inferior axis, a soft-argmax head regresses the top and bottom L3 slice, and a presence head gates out slices that contain no L3. Applied as a sliding window over the full volume.
• Muscle segmenter (muscle_seg.pth) — a 3D UNet (no-new-UNet / nnU-Net, full-resolution configuration) run with sliding-window inference, loaded and driven directly in Python.

Usage

# full pipeline: localize L3 -> crop -> segment -> strip fat -> total muscle seg
myol3 -i fullbody_ct.nii.gz -o total_muscle_seg.nii.gz

# also save the crop, the 4-compartment map, and per-muscle/side metrics
myol3 -i fullbody_ct.nii.gz -o total_muscle_seg.nii.gz \
      --save-crop l3_crop.nii.gz --save-comp composition.nii.gz --save-metrics metrics.json

# already cropped to L3 yourself -> skip localization
myol3 -i my_l3_crop.nii.gz -o total_muscle_seg.nii.gz --cropped

# localize + crop only (no segmentation model needed)
myol3 -i fullbody_ct.nii.gz --save-crop l3_crop.nii.gz

# overrides
myol3 -i ct.nii.gz -o seg.nii.gz \
      --localizer-ckpt /path/l3_localizer.pt \
      --segmenter-ckpt /path/muscle_seg.pth \
      --pad 2 --device cuda

Flag	Meaning
-i, --input	full-body CT (required)
-o, --output	total muscle seg (fat stripped); omit to only localize/crop
--cropped	input is already cropped to L3; skip the localizer
--save-crop	also write the L3-cropped CT
--save-comp	also write the 4-compartment map (muscle*10 + compartment)
--save-metrics	also write per-muscle/side metrics (.json)
--localizer-ckpt / --segmenter-ckpt	checkpoint overrides
--pad	extra slices each side of the L3 crop
--device	cpu or cuda (auto if unset)

Python API:

import myol3
myol3.run("fullbody_ct.nii.gz", "total_muscle_seg.nii.gz",
          save_crop="l3_crop.nii.gz", save_metrics="metrics.json")

Configuration

myol3/config.py:

• TARGET_SPACING — spacing (mm) to resample the input to; set to what the models were trained at (None = keep native).
• LOCALIZER_HW / LOCALIZER_WIN / LOCALIZER_STRIDE — localizer input size and sliding-window params.
• LOCALIZER_MAX_MM — hard cap on the L3 crop length (mm) so a spurious vote can't stretch it.
• WL / WW — CT window for the localizer.
• FAT_HU / FAT_ERODE — intramuscular-fat threshold and interior-core erosion for fat stripping.
• LAMA_HI — muscle / low-attenuation split HU; None = per-scan, per-muscle Gaussian Mixture Model (GMM) (adaptive).
• SEG_TILE_STEP / SEG_USE_MIRRORING — nnU-Net sliding-window step and test-time mirroring.