Update README.md

Author: vpchung

README.md

@@ -1,6 +1,6 @@
 # Evaluation Workflow for BraTS 2023+
 
-The repository contains the evaluation workflows for the [BraTS 2023 challenge and beyond],
+The repository contains the evaluation workflows for the [BraTS 2023 Challenge and beyond],
 including:
 
 * BraTS 2023
@@ -16,71 +16,72 @@ are located in the `shared` folder.
 Source code of the metrics computations mentioned in the README are available
 in the `evaluation` folder of this repo, organized into sub-folders by task.
 
-[BraTS 2023 challenge and beyond]: https://www.synapse.org/brats
+[BraTS 2023 Challenge and beyond]: https://www.synapse.org/brats
 
-## BraTS 2024
 
-Branch: `main`
+## Metrics Overview
 
-BraTS 2024 is an extension to [BraTS 2023](#brats-2023), and will also follow the two
-evaluation phases approach.
+<details><summary><strong>BraTS 2023</strong></summary>
+<br/>
 
 Metrics returned and used for ranking will depend on the task:
 
 **Task** | **Metrics** | **Ranking**
 --|--|--
 Segmentations | Lesion-wise dice, lesions-wise Hausdorff 95% distance (HD95), full dice, full HD95, sensitivity, specificity | Lesion-wise dice, lesion-wise HD95
-Inpainting | Structural similarity index measure (SSIM), peak-signal-to-noise-ratio (PSNR), mean-square-error (MSE) | All 3 metrics
-Augmentations | Full dice, full HD95, sensitivity, specificity | Dice mean, Dice GINI index, HD95 mean, HD95 GINI index
-Pathology | Matthews correlation coefficient (MCC), F1, sensitivity, specificity | All 4 metrics
+Inpainting | Structural similarity index measure (SSIM), peak-signal-to-noise-ratio (PSNR), mean-square-error (MSE) | SSIM, PSNR, MSE
+Augmentations | Full dice, full HD95, sensitivity, specificity | Dice mean, dice variance, HD95 mean, HD95 variance
 
-## BraTS 2023
+---
 
-Branch: `brats2023`
+</details>
 
-BraTS 2023 is split into two evaluation phases:
 
-* **Validation phase:** participants submit <u>predictions files</u> (segmentation masks, t1n inferences, etc.) to be evaluated using the validation dataset
+<details><summary><strong>BraTS-GoAT 2024</strong></summary>
+<br/>
 
-* **Test phase:** participants submit <u>MLCube models</u> that will generate prediction files using the test dataset
+Metrics returned and used for ranking are:
 
-Metrics returned and used for ranking will depend on the task:
+**Metrics** | **Ranking**
+--|--
+Lesion-wise dice, lesions-wise Hausdorff 95% distance (HD95), full dice, full HD95, sensitivity, specificity | Lesion-wise dice, lesion-wise HD95
 
-**Task** | **Metrics** | **Ranking**
---|--|--
-Segmentations | Lesion-wise dice, lesions-wise Hausdorff 95% distance (HD95), full dice, full HD95, sensitivity, specificity | Lesion-wise dice, lesion-wise HD95
-Inpainting | Structural similarity index measure (SSIM), peak-signal-to-noise-ratio (PSNR), mean-square-error (MSE) | SSIM, PSNR, MSE
-Augmentations | Full dice, full HD95, sensitivity, specificity | Dice mean, dice variance, HD95 mean, HD95 variance
+---
 
-## BraTS-GoAT 2024
+</details>
 
-Branch: `brats_goat2024`
 
-Similar to BraTS 2023, BraTS-GoAT 2024 is split into two evaluation phases:
+<details><summary><strong>FeTS 2024</strong></summary>
+<br/>
 
-* **Validation phase:** participants submit <u>segmentation predictions</u> to be evaluated using the validation dataset
 
-* **Test phase:** participants submit <u>MLCube models</u> that will generate segmentation predictions using the test dataset
+Metrics returned are: lesion-wise dice, lesions-wise Hausdorff 95% distance
+(HD95), full dice, full HD95, sensitivity, specificity
 
-Metrics returned and used for ranking are:
+**Note**: Code submission evaluations and ranking were handled by the 
+[FeTS-AI Task 1 infrastructure](https://github.com/FeTS-AI/Challenge/tree/main/Task_1).
 
-**Metrics** | **Ranking**
---|--
-Lesion-wise dice, lesions-wise Hausdorff 95% distance (HD95), full dice, full HD95, sensitivity, specificity | Lesion-wise dice, lesion-wise HD95
+---
+
+</details>
 
-## FeTS 2024
 
-Branch: `fets2024`
+<details><summary><strong>BraTS 2024</strong></summary>
+<br/>
 
-FeTS 2024 has one evaluation phase facilitated by this repo:
+Metrics returned and used for ranking will depend on the task:
 
-* **Validation phase:** participants submit <u>segmentation predictions</u> to be evaluated using the validation dataset
+**Task** | **Metrics** | **Ranking**
+--|--|--
+Segmentations | Lesion-wise dice, lesions-wise Hausdorff 95% distance (HD95), full dice, full HD95, sensitivity, specificity | Lesion-wise dice, lesion-wise HD95
+Inpainting | Structural similarity index measure (SSIM), peak-signal-to-noise-ratio (PSNR), mean-square-error (MSE) | All 3 metrics
+Augmentations | Full dice, full HD95, sensitivity, specificity | Dice mean, Dice GINI index, HD95 mean, HD95 GINI index
+Pathology | Matthews correlation coefficient (MCC), F1, sensitivity, specificity | All 4 metrics
 
-Metrics returned are: lesion-wise dice, lesions-wise Hausdorff 95% distance (HD95), full dice, full HD95, sensitivity, specificity
+---
 
-The **Code submission phase** is handled by the [FeTS-AI Task 1 infrastructure].
+</details>
 
-[FeTS-AI Task 1 infrastructure]: https://github.com/FeTS-AI/Challenge/tree/main/Task_1
 
 ## Kudos 🍻