Update README.md

README.md

@@ -6,7 +6,6 @@ tags:
   - ct
   - lower-extremity
   - body-composition
-  - nnunet
   - computer-vision
 pipeline_tag: image-segmentation
 ---
@@ -15,21 +14,22 @@ pipeline_tag: image-segmentation
 
 **GitHub:** [https://github.com/gogochen07/LegSegNet](https://github.com/gogochen07/LegSegNet)
 
-**LegSegNet** is a deep learning system for lower-extremity CT tissue segmentation and body composition quantification. Given an input CT scan, LegSegNet segments four tissue compartments: **bone**, **skeletal muscle**, **subcutaneous adipose tissue (SAT)**, and **inter- and intramuscular adipose tissue (IMAT)**.
+**LegSegNet** is a deep learning system for lower-extremity CT tissue segmentation and body composition quantification. 
 
-The system can further convert predicted masks into quantitative measurements, including tissue area, tissue volume, CT attenuation, and tissue-volume ratios.
+Given an input CT scan, LegSegNet segments four tissue compartments: **bone**, **skeletal muscle**, **subcutaneous adipose tissue (SAT)**, and **inter- and intramuscular adipose tissue (IMAT)**.
+
+The system can further convert predicted masks into quantitative measurements, including tissue area, tissue volume, CT attenuation, and tissue-volume ratios, supporting downstream medical image analysis.
 
 ## Model Details
 
-- **Task:** Lower-extremity CT tissue segmentation
-- **Input:** Lower-extremity CT images
+- **Task:** Lower extremity CT tissue segmentation
+- **Input:** Lower extremity CT images/volumes
 - **Output:** Multi-class segmentation mask and body composition measurements
-- **Labels:** Background, SAT, skeletal muscle, IMAT, bone
-- **Backbone:** nnU-Net-based segmentation model
+- **Labels:** Background, SAT, skeletal muscle, IAT, bone
 
 ## Usage
 
-Please refer to the GitHub repository for installation instructions, inference scripts, and example commands:
+Please refer to the GitHub repository for more details:
 
 [https://github.com/gogochen07/LegSegNet](https://github.com/gogochen07/LegSegNet)