Update README.md

README.md

@@ -4,19 +4,22 @@ task_categories:
 - image-segmentation
 tags:
 - medical
+- surgical
+- microsurgery
 ---
 
 # MISAW-Seg: Pixel-level Surgical Tool Segmentation in Microsurgical Anastomosis
 
 ## Dataset Overview
 
-MISAW-Seg is a surgical segmentation dataset that extends the original [MISAW dataset](https://www.synapse.org/Synapse:syn21776936/files/) by introducing segmentation annotations for microsurgical tools involved in artificial vessel anastomosis tasks. The original dataset included kinematic data, workflow annotations, and stereo video recordings, but lacked pixel-wise annotations for surgical tools. 
+*MISAW-Seg* is a surgical segmentation dataset that extends the original [MISAW dataset](https://www.synapse.org/Synapse:syn21776936/files/) by introducing segmentation annotations for microsurgical tools involved in artificial vessel anastomosis tasks. The original dataset included kinematic data, workflow annotations, and stereo video recordings, but lacked pixel-wise annotations for surgical tools. 
 
-This dataset provides new segmentation masks created using the Roboflow platform, enabling segmentation research in microsurgical environments. MISAW-Seg is released under the license CC BY-NC 4.0.
+This dataset provides new segmentation masks created using the Roboflow platform, enabling segmentation research in microsurgical environments. *MISAW-Seg* is released under the license CC BY-NC 4.0.
 
-## Data Collection
+## Data Preparation
 
-The MISAW-Seg dataset is constructed by extending the original MISAW dataset, which consists of microsurgical training sessions involving artificial vessel anastomosis. From this dataset, we extracted image frames and manually created semantic segmentation masks for surgical tools.
+The *MISAW-Seg* dataset is constructed by extending the original MISAW dataset, which consists of microsurgical training sessions involving artificial vessel anastomosis. 
+<!--From this dataset, we extracted image frames and manually created semantic segmentation masks for surgical tools.-->
 
 The original MISAW dataset includes kinematic data, stereo video, and workflow annotations. These additional components can be accessed separately at:
 
@@ -27,59 +30,63 @@ In this dataset, we focus on providing:
 - Extracted image frames (460×540 px) from stereo microscope video
 - Corresponding semantic segmentation masks in both PNG and COCO format
 
-Surgical tools were manually annotated on each frame using the Roboflow platform, enabling segmentation tasks in microsurgical environments.
+<!--Surgical tools were manually annotated on each frame using the Roboflow platform, enabling segmentation tasks in microsurgical environments.-->
 
 ## Data Details
 
 Each directory in MISAW-Seg stores raw image data, segmentation masks, and annotation files.
 
-### Directory Structure
-
-```
-MISAW-Seg
-├── images
-│   ├── 1_1
-│   │   ├── 1_1_000000.png
-│   │   └── ...
-│   ├── ...
-├── masks
-│   ├── 1_1_000000.png
-│   └── ...
-├── _annotations.coco.json
-├── fig
-└── README.md
-```
-
-### Image Format
-
-- Extracted from stereo-microscope videos (original resolution: 960×540 px)
-- Left side was cropped to generate 460×540 px frames
-- Frame rate: 30 fps
-
-### Annotation Formats
-
-1. **COCO Format** (`_annotations.coco.json`)
-
-   The COCO-style annotation file contains polygon-based segmentations, bounding boxes, and category IDs for each object.
-
-2. **PNG Format** (`masks/`)
-
-   Pixel-level segmentation masks are provided in PNG format. Each mask shares the same filename as the corresponding image (e.g., `1_1_000030.png`) and stores class IDs as pixel values.
-
-### Segmentation Classes
-
-The dataset contains the following classes:
-
-| Class Number   | Class Name              | RGB Color        |
-|----------------|-------------------------|------------------|
-| 0              | Left artificial vessel  | (0, 255, 0)      |
-| 1              | Left needle holder      | (255, 255, 0)    |
-| 2              | Needle                  | (0, 255, 255)    |
-| 3              | Right artificial vessel | (0, 0, 255)      |
-| 4              | Right needle holder     | (255, 0, 0)      |
-| 5              | Wire                    | (255, 0, 255)    |
-
-
+- **Directory Structure**
+
+    ```
+    MISAW-Seg/
+    ├── images/
+    │   ├── 1_1/
+    │   │   ├── 1_1_000000.png
+    │   │   └── ...
+    │   ├── ...
+    ├── masks/
+    │   ├── 1_1_000000.png
+    │   └── ...
+    ├── _annotations.coco.json
+    ├── fig/
+    └── README.md
+    ```
+
+- **Image Format**
+
+    - Extracted from stereo-microscope videos (original resolution: 960×540 px)
+    - Left side was cropped to generate 460×540 px frames
+    - Frame rate: 30 fps
+
+- **Annotation Formats**
+
+    1. **COCO Format** (`_annotations.coco.json`)
+    
+       The COCO-style annotation file contains polygon-based segmentations, bounding boxes, and category IDs for each object.
+    
+    2. **PNG Format** (`masks/`)
+    
+       Pixel-level segmentation masks are provided in PNG format. Each mask shares the same filename as the corresponding image (e.g., `1_1_000030.png`) and stores class IDs as pixel values.
+
+- **Segmentation Classes**
+
+    The dataset contains the following classes:
+  
+    <center>
+      
+    | Class Number   | Class Name              | RGB Color        |
+    |----------------|-------------------------|------------------|
+    | 0              | Left artificial vessel  | (0, 255, 0)      |
+    | 1              | Left needle holder      | (255, 255, 0)    |
+    | 2              | Needle                  | (0, 255, 255)    |
+    | 3              | Right artificial vessel | (0, 0, 255)      |
+    | 4              | Right needle holder     | (255, 0, 0)      |
+    | 5              | Wire                    | (255, 0, 255)    |  
+
+    </center>
+
+<!--
 ## Annotation Method
 
 Segmentation masks were manually created using the Roboflow platform. Annotators performed frame-level labeling of surgical instruments and maintained consistency across temporal frames.
@@ -95,11 +102,21 @@ The annotation process included the following steps:
 - Final masks were stored in COCO JSON (polygon-based) formats.
 
 The annotators were two non-medical professionals. They followed a consistent labeling guide and used tool appearance and continuity across frames to ensure annotation quality.
+-->
 
 ## Examples of Labeled Data
 
-Figure 1, 2, 3 shows some examples of labeled data.
+Figures 1, 2, and 3 show examples of the dataset with segmentation labels.
+
+<center>
+  
+Figure 1 | Figure 2 | Figure 3
+:-------------------------:|:-------------------------:|:-------------------------:
+![](./fig/ex1.png)  |  ![](./fig/ex2.png) | ![](./fig/ex3.png)
+
+</center>
 
+<!--
 <figure>
     <img src='./fig/ex1.png' width="460" height="540"/>
     <figcaption>Figure 1: Example of Segmentation Mask of Image 1</figcaption>
@@ -114,6 +131,7 @@ Figure 1, 2, 3 shows some examples of labeled data.
     <img src='./fig/ex3.png' width="460" height="540"/>
     <figcaption>Figure 3: Example of Segmentation Mask of Image 3</figcaption>
 </figure>
+-->
 
 ## Citation