Update README Formatting

README.md

@@ -6,16 +6,15 @@ library_name: monai
 license: apache-2.0
 ---
 # Model Overview
-
 Body CT segmentation models are evolving. Starting from abdominal multi-organ segmentation model [1]. Now the community is developing hundreds of target anatomies. In this bundle, we provide re-trained models for (3D) segmentation of 104 whole-body segments.
 
 This model is trained using the SegResNet [3] network. The model is trained using TotalSegmentator datasets [2].
 
-![structures](https://github.com/wasserth/TotalSegmentator/blob/master/resources/imgs/overview_classes.png)
+![structures](https://raw.githubusercontent.com/wasserth/TotalSegmentator/master/resources/imgs/overview_classes.png)
 
 Figure source from the TotalSegmentator [2].
 
-## MONAI Label Showcase
+### MONAI Label Showcase
 
 - We highlight the use of this bundle to use and visualize in MONAI Label + 3D Slicer integration.
 
@@ -58,6 +57,8 @@ One channel
 - Label 0: Background (everything else)
 - label 1-105: Foreground classes (104)
 
+## Resource Requirements and Latency Benchmarks
+
 ### High-Resolution and Low-Resolution Models
 
 We retrained two versions of the totalSegmentator models, following the original paper and implementation.
@@ -71,13 +72,11 @@ In MONAI Label use case, users can set the parameter in 3D Slicer plugin to cont
   - 1.5 mm model: [Download link](https://drive.google.com/file/d/1PHpFWboimEXmMSe2vBra6T8SaCMC2SHT/view?usp=share_link)
   - 3.0 mm model: [Download link](https://drive.google.com/file/d/1c3osYscnr6710ObqZZS8GkZJQlWlc7rt/view?usp=share_link)
 
-### Resource Requirements and Latency Benchmarks
-
 Latencies and memory performance of using the bundle with MONAI Label:
 
 Tested Image Dimension: **(512, 512, 397)**, the slice thickness is **1.5mm** in this case. After resample to **1.5** isotropic resolution, the dimension is   **(287, 287, 397)**
 
-## 1.5 mm (highres) model (Single Model with 104 foreground classes)
+### 1.5 mm (highres) model (Single Model with 104 foreground classes)
 
 Benchmarking on GPU: Memory: **28.73G**
 
@@ -87,7 +86,7 @@ Benchmarking on CPU: Memory: **26G**
 
 - `++ Latencies => Total: 38.3108; Pre: 1.6643; Inferer: 30.3018; Invert: 0.0000; Post: 6.1656; Write: 0.1786`
 
-## 3.0 mm (lowres) model (single model with 104 foreground classes)
+### 3.0 mm (lowres) model (single model with 104 foreground classes)
 
 GPU: Memory: **5.89G**
 
@@ -99,13 +98,13 @@ CPU: Memory: **2.3G**
 
 ## Performance
 
-- 1.5 mm Model Training
+### 1.5 mm Model Training
 
-  - Training Accuracy
+#### Training Accuracy
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_wholeBody_ct_segmentation_train_accuracy.png) <br>
 
-  - Validation Dice
+#### Validation Dice
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_wholeBody_ct_segmentation_15mm_validation.png) <br>
 

configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.1.3",
+    "version": "0.1.4",
     "changelog": {
+        "0.1.4": "Update README Formatting",
         "0.1.3": "add non-deterministic note",
         "0.1.2": "Update figure with links",
         "0.1.1": "adapt to BundleWorkflow interface and val metric",

docs/README.md

@@ -1,14 +1,13 @@
 # Model Overview
-
 Body CT segmentation models are evolving. Starting from abdominal multi-organ segmentation model [1]. Now the community is developing hundreds of target anatomies. In this bundle, we provide re-trained models for (3D) segmentation of 104 whole-body segments.
 
 This model is trained using the SegResNet [3] network. The model is trained using TotalSegmentator datasets [2].
 
-![structures](https://github.com/wasserth/TotalSegmentator/blob/master/resources/imgs/overview_classes.png)
+![structures](https://raw.githubusercontent.com/wasserth/TotalSegmentator/master/resources/imgs/overview_classes.png)
 
 Figure source from the TotalSegmentator [2].
 
-## MONAI Label Showcase
+### MONAI Label Showcase
 
 - We highlight the use of this bundle to use and visualize in MONAI Label + 3D Slicer integration.
 
@@ -51,6 +50,8 @@ One channel
 - Label 0: Background (everything else)
 - label 1-105: Foreground classes (104)
 
+## Resource Requirements and Latency Benchmarks
+
 ### High-Resolution and Low-Resolution Models
 
 We retrained two versions of the totalSegmentator models, following the original paper and implementation.
@@ -64,13 +65,11 @@ In MONAI Label use case, users can set the parameter in 3D Slicer plugin to cont
   - 1.5 mm model: [Download link](https://drive.google.com/file/d/1PHpFWboimEXmMSe2vBra6T8SaCMC2SHT/view?usp=share_link)
   - 3.0 mm model: [Download link](https://drive.google.com/file/d/1c3osYscnr6710ObqZZS8GkZJQlWlc7rt/view?usp=share_link)
 
-### Resource Requirements and Latency Benchmarks
-
 Latencies and memory performance of using the bundle with MONAI Label:
 
 Tested Image Dimension: **(512, 512, 397)**, the slice thickness is **1.5mm** in this case. After resample to **1.5** isotropic resolution, the dimension is   **(287, 287, 397)**
 
-## 1.5 mm (highres) model (Single Model with 104 foreground classes)
+### 1.5 mm (highres) model (Single Model with 104 foreground classes)
 
 Benchmarking on GPU: Memory: **28.73G**
 
@@ -80,7 +79,7 @@ Benchmarking on CPU: Memory: **26G**
 
 - `++ Latencies => Total: 38.3108; Pre: 1.6643; Inferer: 30.3018; Invert: 0.0000; Post: 6.1656; Write: 0.1786`
 
-## 3.0 mm (lowres) model (single model with 104 foreground classes)
+### 3.0 mm (lowres) model (single model with 104 foreground classes)
 
 GPU: Memory: **5.89G**
 
@@ -92,13 +91,13 @@ CPU: Memory: **2.3G**
 
 ## Performance
 
-- 1.5 mm Model Training
+### 1.5 mm Model Training
 
-  - Training Accuracy
+#### Training Accuracy
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_wholeBody_ct_segmentation_train_accuracy.png) <br>
 
-  - Validation Dice
+#### Validation Dice
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_wholeBody_ct_segmentation_15mm_validation.png) <br>