SwinUNETR_body_segmentation

<h1 style="border-bottom: 2px solid black; font-size: 100px;" align="center"> SwinUNETR </h1>

Trained by Margerie Huet Dastarac . <br> Training date: November 2023 .

1. Task Description

Segmentation of the body on the CT scan on a dataset of 60 oropharyngeal patients. This model can be used to clean CT scans by setting voxels value outside of the body contour to air, a typical preprocessing step for other networks.

2. Model

2.1. Architecture

<img width="100%" src="https://cdn-uploads.huggingface.co/production/uploads/65c9dbefd6cbf9dfed67367e/o59In69BqrxTEoOdSHZLD.png" alt="alternatetext">

Figure 1: SwinUNETR architecture

2.2. Input

<ul> <li> CT</li> </ul>

2.3. Output

<ul> <li> BODY</li> </ul>

2.4 Training details

<ul> <li> Number of epoch: 300 </li> <li> Loss function: Dice loss </li> <li> Optimizer: Adam </li> <li> Learning Rate: 3e-4 </li> <li> Dropout: No </li> <li> Patch size in voxels: (128,128,128) </li> <li> Data augmentation used: <ul> <li> RandSpatialCropd</li> <li> RandFlipd axis:0</li> <li> RandFlipd axis:1</li> <li> RandFlipd axis:2</li> <li> NormalizeIntensityd</li> <li> RandScaleIntensityd factors:0.1 prob:1.0</li> <li> RandShiftIntensityd, offsets:0.1, prob:1.0</li> </ul> </li> </ul>

3. Dataset

<ul> <li> Location: Head and neck, oropharynx </li> <li> Training set size: 60 </li> <li> Resolution in mm: 3x3x3 </li> </ul>

Performance

• TBD