Observer Choice and Threshold Selection in Retinal Vessel Segmentation: A Subject-Separated Evaluation

Wenhao Xu; Yixian Kong; Ting Pan; Feilong Wang; Changwei Wang

cs.CVSep 22, 2026

Observer Choice and Threshold Selection in Retinal Vessel Segmentation: A Subject-Separated Evaluation

Authors: Wenhao Xu, Yixian Kong, Ting Pan, Feilong Wang, Changwei Wang

Organizations: Zhengzhou Police University, Zhengzhou, China · School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China · Qilu University of Technology and Jinan Supercomputing Center, Jinan, China

Abstract

The annotation used to select a segmentation threshold is part of the evaluation protocol, yet its effect is easily conflated with model quality. We examine this choice for retinal vessel segmentation using all 28 CHASE DB1 images and both human annotations. A fixed seven-fold protocol keeps both eyes of each of the 14 subjects together. Random forests and Extra Trees are fitted against observer 1 with three random seeds, yielding 42 fits. Five threshold policies share identical score maps: fixed 0.50, observer-1 tuning, observer-2 tuning, mean-observer tuning, and maximin tuning of the per-image lower observer Dice. For random forests, maximin changes the threshold in 19 of 21 fits, but worst-observer Dice decreases from 70.53 percent to 70.45 percent. The paired difference is -0.073 percentage points, with a conditional subject-bootstrap 95 percent interval of [-0.384, 0.238]. Extra Trees shows the same direction. Identical observer-1-tuned random-forest masks score 73.66 percent against observer 1 and 71.06 percent against observer 2. The results support explicit reporting of both the threshold-selection reference and evaluation reference; they do not support an accuracy benefit from maximin tuning in this cohort. All splits, raw predictions, metrics and code are supplied. AI assistance is disclosed.

Figures & tables

Figure 1: The fixed seven-fold allocation. Each column represents a subject and both eyes. The four validation images select the threshold; the four test images are scored only after that choice is recorded.

Estimator	Threshold policy	Dice O1	Dice O2	$W$	95% interval for $W$
RF	Fixed 0.50	61.94	59.87	58.21	55.61–60.61
RF	O1-tuned	73.66	71.06	70.53	69.15–71.95
RF	O2-tuned	73.62	71.04	70.55	69.31–71.85
RF	Mean-tuned	73.64	71.04	70.53	69.21–71.92
RF	Maximin	73.47	70.91	70.45	69.25–71.76
ET	Fixed 0.50	61.70	59.63	58.03	55.39–60.51

Table 1: Native-resolution, whole-image evaluation. Values are percentages, averaged over eyes and seeds within subjects.

W

averages the lower observer Dice per image; it is not the smaller of the two displayed Dice columns. Intervals condition on fitted predictions.

Figure 2: Every subject’s change in worst-observer Dice, after averaging both eyes and three seeds. Positive values favor maximin; negative values favor O1-tuned. Subject IDs follow the public data filenames.

Figure 3: Predetermined RF seed-17 examples. Each panel shows the same $400\times 400$ crop, rows 230–629 and columns 290–689 in zero-based coordinates. The original photographs are cropped only for display; masks are the supplied annotations or actual held-out predictions. Image credit: CHASE_DB1 creators [ 3 ] , CC BY 4.0. No image synthesis or generative editing is used.

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ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image

Mingzhi Xu, Yizhe Zhang

Nanjing University of Science and Technology, Nanjing, China

Vessel Segmentation Semi-Supervised Medical Image Segmentation Semi-Supervised Learning Dinov3

Open paper AskPDF arXiv

Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly. We study retinal vessel segmentation in an extreme semi-supervised setting with one annotated image and a pool of unlabeled images. We propose ESRVS, which selects a representative reference image for manual annotation and transfers vessel cues using target-domain-adapted DINOv3 features. ESRVS constructs a multi granular vessel prototype, combines prototype-similarity maps with a physics-inspired prior to generate initial pseudo-labels, and refines the transferred supervision through weighted pseudo-label training and adversarial refinement. Across eight public datasets, ESRVS achieves the best Dice and clDice on six datasets, and the best HD95 on all eight datasets among the compared semi-supervised methods, although those methods use 10 to 20% labeled data. With Mask2Former, ESRVS retains on average 93.7% of fully supervised Dice and 95.1% of fully supervised clDice. These results demonstrate the potential of foundation-model label propagation for highly label-efficient retinal vessel segmentation. Code is available at https://github.com/IAANNH/ESRVS.

Abstract

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