Tongue Segmentation Eval

tongue-segmentation-eval

TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot — Cao et al. (2023) (arXiv:2308.06444, 2023)

What this evaluates

Evaluates zero-shot and cross-dataset generalization of a tongue segmentation model adapted from SAM. It probes the model's ability to segment tongue regions in medical images without task-specific fine-tuning on the target datasets.

Datasets

• TongueSet1 — total 3192; splits: train (-1), test (-1)
• BioHit (TongueSet2) — total 300; splits: test (300)
• Webset (TongueSet3) — total 1000; splits: test (1000)

Metrics

• mIoU (primary) — range: [0, 1]
  • Mean Intersection over Union across all pixels or classes. IoU = TP / (TP + FP + FN). Averaged over the dataset.
• mPA — range: [0, 1]
  • Mean Pixel Accuracy across classes. PA = TP / (TP + FP). Averaged over the dataset.
• Acc — range: [0, 1]
  • Overall pixel accuracy. Acc = (TP + TN) / Total pixels.

Input / output format

Input: RGB tongue images of varying resolutions (e.g., 400×400, 768×576, or arbitrary sizes).

Output: Binary segmentation mask indicating the tongue region.

Scoring recipe

def compute_metrics(pred_mask, gt_mask):
    intersection = np.logical_and(pred_mask, gt_mask).sum()
    union = np.logical_or(pred_mask, gt_mask).sum()
    mIoU = intersection / union if union > 0 else 0.0
    mPA = (intersection / np.sum(gt_mask)) if np.sum(gt_mask) > 0 else 0.0
    Acc = np.mean(pred_mask == gt_mask)
    return mIoU, mPA, Acc

Common pitfalls

• Zero-shot evaluation trains exclusively on TongueSet1 and tests on TongueSet2/3 without fine-tuning, which may overstate generalization if domain shift is large.
• The evaluation pipeline relies on an object detector (YOLOX) to generate bounding box prompts; performance is thus conflated with detection quality rather than pure segmentation capability.
• Image sizes vary significantly across datasets and are not explicitly normalized before metric computation, which can bias pixel-based metrics like mPA and Acc.

Evidence (verbatim from paper)

In this study, we utilized three evaluation metrics to assess the performance of the model: mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and Accuracy (Acc). TongueSet1 is used as the training set, while TongueSet2 and TongueSet3 are used as the test sets.

Citation

@misc{cao2023tonguesam,
  title={TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot},
  author={Cao et al. (2023)},
  year={2023},
  note={arXiv:2308.06444}
}

• arXiv: 2308.06444