What are the evaluation indicators for image segmentation?

The evaluation indicators for image segmentation are metrics used to measure the accuracy and performance of a segmentation model. Common indicators include:

1. Intersection over Union (IoU) / Jaccard Index: Measures the overlap between the predicted segmentation and the ground truth. Formula:

   $IoU = \frac{Area\ of\ Intersection}{Area\ of\ Union} = \frac{TP}{TP + FP + FN}$

   Example: In medical image segmentation, a high IoU indicates better tumor boundary detection.

2. Pixel Accuracy (PA): The ratio of correctly classified pixels to the total pixels. Formula:

   $PA = \frac{TP + TN}{TP + TN + FP + FN}$

   Example: In satellite image segmentation, PA helps evaluate land cover classification accuracy.

3. Mean Intersection over Union (mIoU): The average IoU across all classes, useful for multi-class segmentation.
   Example: In autonomous driving, mIoU assesses road, pedestrian, and vehicle segmentation performance.

4. Dice Coefficient (F1 Score for Segmentation): Measures overlap, weighted by false positives and false negatives. Formula:

   $Dice = \frac{2 \times TP}{2 \times TP + FP + FN}$

   Example: In histopathology image analysis, Dice evaluates cell segmentation accuracy.

5. Boundary F1 Score: Evaluates the precision and recall of segmentation boundaries, critical for edge-sensitive tasks.
   Example: In aerial image segmentation, it ensures precise building outline detection.

For cloud-based image segmentation solutions, Tencent Cloud offers AI-powered services like TI Platform and TI-ONE, which provide pre-trained models and scalable computing resources for efficient segmentation tasks. These services support custom model training and deployment, optimizing performance metrics like IoU and mIoU.