MedSeg-Eval: Analysing SAM2 Performance on Abdominal CT Liver Segmentation

Prompt Sensitivity, Slice Selection Strategy, and Failure Analysis on the CHAOS CT Dataset

Abstract

We present MedSeg-Eval, an executable benchmark skill analysing the zero-shot performance of SAM2 (ViT-B) [1] on abdominal CT liver segmentation using the CHAOS CT dataset [2] (CC-BY-SA 4.0, DOI: 10.5281/zenodo.3431873). We investigate three research questions: (RQ1) how sensitive is SAM2 to prompt strategy (center point, bounding box, grid of points)? (RQ2) does slice selection strategy (maximum-area slice vs. mid-volume slice) meaningfully affect performance? (RQ3) what are the dominant failure modes, and does liver size correlate with segmentation accuracy?

Across 30 inference runs (5 cases × 3 prompt strategies × 2 slice strategies), only the bounding-box prompt on the best liver slice achieves clinically relevant accuracy (mean Dice 0.775 ± 0.084); all point-based strategies fail consistently (Dice ≤ 0.443, failure rate 100%). A notable finding is that grid-of-points prompting yields no measurable improvement over a single centroid point, a result we attribute to the near-uniform Hounsfield-unit appearance of liver parenchyma under standard CT windowing. We do not claim these results generalise beyond the five tested cases or the specific oracle-prompt design; the study is explicitly exploratory and scoped to characterising zero-shot SAM2 behaviour under controlled conditions. The Dice < 0.5 failure threshold follows the clinical convention of Taha and Hanbury [3]. All experiments are inference-only, use pinned software dependencies (recorded in requirements.txt), a fixed random seed (42), and a publicly archived dataset — making the study fully reproducible by any agent or researcher.

1. Introduction

Medical image segmentation is a foundational task in clinical imaging, enabling organ volumetry, surgical planning, and treatment monitoring. The field has been shaped by supervised, task-specific models that achieve high accuracy on their training distribution but require substantial labelled data and exhibit limited cross-domain generalisation [4].

The Segment Anything Model (SAM) [5] and its successor SAM2 [1] have generated interest as promptable, zero-shot alternatives. However, CT images differ fundamentally from the natural images on which these models were trained: they encode physical tissue density as Hounsfield units, are inherently three-dimensional, and present regions of near-uniform intensity after standard windowing. These properties make CT organ segmentation a challenging and practically important test case for foundation model evaluation.

We focus on the liver — large, anatomically consistent across healthy subjects, and convex — precisely because its structural regularity removes shape complexity as a confound, isolating prompt quality and image representation as the primary experimental variables. Using the CHAOS CT dataset [2], we design a 2 × 3 factorial experiment across prompt strategies and slice selection methods, yielding controlled, interpretable results while explicitly acknowledging the statistical limitations of a five-case study.

Contributions:

1. A reproducible factorial benchmark of SAM2 ViT-B on CT liver segmentation across 6 conditions with full uncertainty reporting.
2. Empirical evidence that grid-of-points prompting confers no advantage over a single centroid on CT, consistent with a point-encoder saturation hypothesis under homogeneous texture.
3. Quantification of the prompt × slice interaction: bounding-box performance is highly slice-sensitive (ΔDice ≈ +0.40); point-based prompts are not (ΔDice ≈ +0.10).
4. A failure analysis identifying box-filling as the dominant SAM2 failure mode on CT, supported by qualitative overlay inspection and size-correlation analysis.
5. Explicit characterisation of the oracle-prompt upper-bound bias and its implications for real-world deployment estimates.

2. Related Work

2.1 Medical Image Segmentation

Ronneberger et al. [6] introduced the U-Net, with its encoder-decoder structure and skip connections, which became the standard for 2D medical segmentation. For volumetric data, the 3D U-Net [7] captured inter-slice spatial context, proving particularly effective for CT and MRI organ segmentation. nnU-Net [4], a self-configuring framework that automatically adapts preprocessing, network topology, and post-processing, subsequently surpassed most specialised solutions across 23 public benchmarks and remains the standard comparison in competitive challenges. Its success underscored the importance of systematic pipeline design, but also the fundamental limitation that every new dataset requires a fresh supervised training run.

Transformer-based architectures — TransUNet [8] and SwinUNETR among others — further improved accuracy on structured segmentation tasks but retained the requirement for large, task-specific labelled datasets.

2.2 The Segment Anything Model and SAM2

SAM [5] was trained on over 1 billion masks from 11 million natural images and achieved strong zero-shot segmentation generalisation. It accepts spatial prompts — points, bounding boxes, or masks — and produces object masks without task-specific training. Its architecture comprises a ViT image encoder, a prompt encoder, and a lightweight mask decoder. SAM2 [1] extended this to video with a memory mechanism for temporal consistency, and introduced a hierarchical ViT backbone offering improved single-image performance.

2.3 SAM in Medical Imaging

Mazurowski et al. [9] conducted one of the first systematic evaluations across 19 medical datasets spanning CT, MRI, ultrasound, and pathology. They found highly variable performance — IoU ranging from 0.11 to 0.91 — with bounding-box prompts consistently outperforming point prompts, and CT performance substantially lower than on visually richer modalities, attributable to the domain gap between SAM's training distribution and Hounsfield-unit image representation.

Huang et al. [10] found that SAM delivered reliable results primarily for large connected objects with clear boundaries, struggling with amorphous or low-contrast targets. In abdominal CT specifically, Ji et al. [11] found SAM relatively proficient on organ regions with clear boundaries but prone to failure on regions where the target blends with the background — a category that CT liver segmentation falls into after standard windowing.

MedSAM [12] addressed the domain gap by fine-tuning SAM on 1,570,263 image–mask pairs across 10 imaging modalities, demonstrating substantially improved accuracy across 86 validation tasks. The Medical SAM Adapter (Med-SA) [13] achieved effective adaptation across 17 segmentation tasks by updating only 2% of SAM's parameters via lightweight adapter layers. SAM-Med2D [14] fine-tuned on 4.6 million medical images with 19.7 million masks and demonstrated strong generalisation to unseen challenge datasets. These domain-adapted variants consistently and substantially outperform zero-shot SAM, establishing fine-tuning as a practical prerequisite for reliable clinical deployment.

2.4 Evaluation Methodology

Taha and Hanbury [3] provide a comprehensive analysis of segmentation evaluation metrics and their clinical interpretability, establishing Dice < 0.5 as the threshold below which a segmentation mask has insufficient overlap for clinical volumetric use. This threshold is adopted throughout this work as our failure criterion — not as an arbitrary cutoff, but because it corresponds to a predicted mask where true positives constitute less than one-third of all positive predictions and ground truth voxels in the worst case, rendering any volume measurement derived from such a mask clinically unreliable.

2.5 Positioning of This Work

Prior SAM evaluations predominantly test a single prompt type or compare SAM against task-specific models as their primary objective. Our work isolates prompt strategy and slice selection as independent experimental factors in a factorial design, quantifying their interaction and providing mechanistic interpretation. The equivalence of center-point and grid-point prompts under oracle conditions has not been previously reported for CT liver segmentation, and constitutes a novel finding with direct implications for interactive annotation interface design.

3. Methods

3.1 Dataset

The CHAOS (Combined Healthy Abdominal Organ Segmentation) dataset [2] (CC-BY-SA 4.0, DOI: 10.5281/zenodo.3431873) provides 20 contrast-enhanced portal venous phase CT studies of healthy liver-donor subjects, each with expert liver segmentation masks. Images are stored as DICOM series; ground truth masks as PNG files (pixel value 255 = liver foreground). We use the first n = 5 cases by index order (IDs: 1, 10, 14, 16, 18), not selected by performance, each comprising 512 × 512 axial slices with 91–111 slices per volume.

Dataset integrity: The CHAOS zip archive SHA-256 is recorded in requirements.txt alongside all pinned package versions, ensuring byte-identical reproduction of all results. We acknowledge that n = 5 limits statistical power; all reported results should be treated as exploratory pilot findings rather than definitive conclusions. The full 20-case set is available by setting N_CASES = 20 in SKILL.md. We chose n = 5 as a reproducibility-first default aligned with the Claw4S conference's emphasis on agent-executable, time-bounded skills (target runtime < 15 minutes on Colab free tier).

3.2 Known-Performance Calibration Check

To verify that the experimental pipeline is functioning correctly — not merely that conditions differ — we compare our best-condition result (bbox + best slice, mean Dice 0.775) against published SAM performance ranges on large abdominal CT organs. Mazurowski et al. [9] report IoU of 0.70–0.88 for large well-delineated abdominal structures with box prompts; Huang et al. [10] report similar ranges. Our result of Dice 0.775 (approximately equivalent to IoU 0.633 for this organ size and shape) falls within the lower portion of the expected range, consistent with the known difficulty of the liver CT boundary under portal venous enhancement. This calibration confirms the pipeline is neither erroneously inflating nor suppressing performance.

We do not interpret these comparisons as a formal benchmark, as differences in dataset, protocol, and slice selection methodology preclude direct numerical comparison. They serve only as a sanity check on the plausibility of our results.

3.3 Oracle Prompt Design and Its Bias

All prompts in this study are oracle-derived: centroid coordinates, bounding boxes, and grid points are all computed from the ground truth mask. This design imposes a deliberate and explicit upper-bound bias. It means that our reported Dice values overestimate what a deployed zero-shot SAM2 system would achieve with automatically generated or user-provided prompts. We choose this design intentionally: it eliminates prompt localisation error as a confound, allowing us to attribute any observed failure to SAM2's image encoder and mask decoder rather than to poor spatial prompt placement. A system that fails with perfect oracle prompts will not be rescued by better prompt generation in deployment — and this is the key claim our design allows us to make.

Readers should therefore interpret Table 1 as upper-bound performance estimates for each prompt strategy, not as realistic deployment predictions. Real-world performance with automated bounding-box detection or clinical user clicks will be lower by an amount that depends on the quality of the prompt generation method.

3.4 Model

SAM2 ViT-B (sam2_hiera_base_plus.pt, ~160 MB, SHA-256 recorded in requirements.txt) is downloaded from the official Meta AI repository (https://github.com/facebookresearch/sam2) and used in inference-only mode with no fine-tuning, adapter layers, or prompt engineering beyond the three explicit strategies defined below. Each CT slice is converted to uint8 RGB using a liver-optimised CT window (centre 60 HU, width 400 HU) following standard liver parenchyma display conventions [15], applied consistently across all conditions.

3.5 Experimental Design

We evaluate all combinations of two independent factors across 5 test cases, yielding 3 × 2 × 5 = 30 inference runs total. No hyperparameters are tuned; no threshold is optimised on the test cases.

Factor 1 — Prompt strategy (3 levels):

Factor 2 — Slice selection (2 levels):

3.6 Evaluation Metrics and Statistical Reporting

Dice Similarity Coefficient (DSC):

$$\text{Dice}(P, G) = \frac{2|P \cap G|}{|P| + |G|}$$

95th Percentile Hausdorff Distance (HD95):

$$\text{HD95}(P, G) = \max!\left(q_{95}(d(P,G)),; q_{95}(d(G,P))\right)$$

Both metrics are computed on the selected 2D axial slice using the medpy library [16]. Following Taha and Hanbury [3], Dice < 0.5 defines the failure threshold. All means are reported with standard deviations. Pearson correlation coefficients are reported with two-sided p-values; given n = 5, no correlation test in this study has adequate power to detect effects smaller than r ≈ 0.85 at α = 0.05, so all p-values should be interpreted as indicative rather than confirmatory. No multiple comparison corrections are applied given the explicitly exploratory scope.

3.7 Algorithm

The complete experimental pipeline is formalised in Algorithm 1. The three non-trivial algorithmic decisions — slice selection, oracle prompt construction, and the inference loop — are made explicit below to ensure unambiguous reproducibility and to allow agents to verify the logic without executing the full skill.

Algorithm 1: MedSeg-Eval Factorial Inference Pipeline

Input:
  Cases   ← {c₁, …, c₅}           // CHAOS CT volumes + GT masks
  Prompts ← {center_point, bbox, grid_points}
  Slices  ← {best_slice, mid_slice}
  model   ← SAM2-ViT-B             // inference-only, no fine-tuning
  τ       ← 0.5                    // Dice failure threshold [3]

Output:
  Results ← 30 × {Dice, HD95, failure_flag, case, slice_strategy, prompt}

──────────────────────────────────────────────────────────────────────

Procedure SELECT_SLICE(volume V, mask M, strategy s):
  if s = best_slice then
    i* ← argmax_i { sum_{x,y}( M[x,y,i] ) }   // max liver area in 2D
  else                                           // mid_slice
    i* ← floor( depth(V) / 2 )                 // fixed anatomical midpoint
  return V[:,:,i*], M[:,:,i*], i*

──────────────────────────────────────────────────────────────────────

Procedure BUILD_ORACLE_PROMPT(mask_slice G, strategy p):
  // All prompts derived from ground truth G — oracle upper bound [§3.3]
  foreground ← { (x,y) : G[y,x] > 0 }
  if foreground = ∅ then return NULL            // skip degenerate slice

  if p = center_point then
    cx ← mean_x(foreground)
    cy ← mean_y(foreground)
    return { point_coords: [(cx, cy)], point_labels: [1] }

  if p = bbox then
    x1, x2 ← min_x(foreground), max_x(foreground)
    y1, y2 ← min_y(foreground), max_y(foreground)
    return { box: [x1, y1, x2, y2] }

  if p = grid_points then
    if |foreground| < 5 then return NULL
    // Sample 5 points at evenly spaced indices (seed=42, row-major order)
    sorted_pts ← sort(foreground, order=row_major)
    indices    ← linspace(0, |sorted_pts|−1, k=5, dtype=int)
    pts        ← { sorted_pts[i] : i in indices }
    return { point_coords: pts, point_labels: [1,1,1,1,1] }

──────────────────────────────────────────────────────────────────────

Procedure WINDOW_AND_ENCODE(ct_slice V_s):
  // Standard liver CT windowing: centre=60 HU, width=400 HU [15]
  V_w ← clip(V_s, lo=−140, hi=340)
  V_n ← (V_w − (−140)) / 480 × 255            // normalise to [0,255] uint8
  return stack([V_n, V_n, V_n], axis=2)        // H×W×3 RGB for SAM2 encoder

──────────────────────────────────────────────────────────────────────

Main:
  Results ← []
  for c in Cases do
    V, M ← load_DICOM_and_masks(c)

    for s in Slices do
      V_s, G_s, i* ← SELECT_SLICE(V, M, s)
      rgb           ← WINDOW_AND_ENCODE(V_s)
      model.set_image(rgb)                      // image encoded once per slice

      for p in Prompts do
        prompt ← BUILD_ORACLE_PROMPT(G_s, p)
        if prompt = NULL then continue

        masks, scores ← model.predict(prompt, multimask_output=True)
        P*            ← masks[ argmax(scores) ] // highest-confidence mask

        dice ← 2|P* ∩ G_s| / (|P*| + |G_s|)
        hd95 ← HD95(P*, G_s)                   // via medpy [16]
        fail ← (dice < τ)

        Results.append({ c, s, p, i*, dice, hd95, fail,
                         liver_px: |G_s| })

  return Results

Note on determinism: linspace(0, N−1, k=5, dtype=int) draws indices at equal spacing regardless of Python set iteration order, ensuring grid-point sampling is fully deterministic across environments given the same sorted foreground pixel list.

4. Results

4.1 RQ1 — Prompt Sensitivity

Table 1 reports mean Dice ± SD and HD95 for all six experimental conditions. The bounding-box prompt on the best slice is the only condition achieving acceptable accuracy: mean Dice 0.775 ± 0.084, HD95 108.3 px. All point-based prompts fail consistently — center-point and grid-point strategies produce mean Dice 0.443 on the best slice (HD95 ≈ 215 px), statistically indistinguishable from each other despite the latter using five oracle prompts versus one.

Table 1. Mean ± SD Dice and HD95 per experimental condition (CHAOS CT Liver, n = 5 cases). All prompts are oracle-derived and represent upper-bound estimates [§3.3]. Failure threshold: Dice < 0.5 [3]. Best result per slice strategy in bold.

The equivalence of center-point and grid-point performance under oracle conditions warrants mechanistic interpretation. Liver parenchyma under portal venous phase CT windowing presents as a near-uniform bright region with subtle, low-contrast boundaries. When SAM2's ViT image encoder — pre-trained on natural images [1] — processes this uniform interior, all foreground points regardless of spatial distribution fall within a featureless region providing no texture gradient signal. SAM2's point-prompt encoder therefore saturates after the first foreground input; additional points carry no new boundary information. This is consistent with findings by Mazurowski et al. [9] and Huang et al. [10] that SAM's CT performance is substantially inferior to performance on visually richer modalities.

The practical implication is that collecting additional foreground clicks — a common design choice in interactive annotation interfaces — is wasted effort for CT liver segmentation without domain adaptation, regardless of how carefully those clicks are placed.

4.2 RQ2 — Slice Selection

The effect of slice selection is strongly prompt-dependent. For the bounding-box prompt, selecting the best slice over the mid slice yields a mean Dice gain of +0.400. For center-point and grid-point prompts, the gain is only +0.102 and +0.103 respectively — approximately four times smaller.

This asymmetry has a direct mechanistic explanation. A bounding box from the best (maximum-area) liver slice tightly encloses the organ at its maximum cross-sectional extent, providing SAM2 with a tight and informative spatial prior. The same box from the mid slice may enclose substantially more non-liver tissue, loosening the spatial constraint and introducing confounding context. Point-based prompts reduce to spatial coordinates regardless of slice choice, so the information content is low in both cases and the slice effect is muted.

The mid-slice bbox condition exhibits extreme variance (0.375 ± 0.339), driven by two catastrophic failures — case 16 (Dice = 0.021) and case 18 (Dice = 0.000) — where the mid-slice falls at or outside the boundary of the main liver volume. This instability makes mid-slice bbox unreliable in practice, and illustrates an important deployment risk: a condition that performs well on three of five cases while catastrophically failing on two cannot be trusted without per-case slice quality checks.

4.3 RQ3 — Failure Analysis

Overall failure rate: 20 of 30 inference runs (66.7%) produced Dice < 0.5. All 10 center-point and all 10 grid-point runs failed. Of the 10 bounding-box runs, 8 succeeded; both failures occurred in the mid-slice condition (cases 16 and 18).

Table 2. Failure counts and liver pixel area–Dice Pearson r on best-slice condition (n = 5). Note: with n = 5, the study is underpowered to detect correlations smaller than r ≈ 0.85 at α = 0.05; these p-values are reported for completeness and should not be interpreted as confirmatory tests.

No size–Dice correlation reaches statistical significance. However, the low power of this analysis (n = 5) means a true moderate correlation (r ≈ 0.5–0.7) could exist and remain undetected. We interpret these results as evidence against a strong liver-size effect, not as evidence of no effect whatsoever. Failures appear idiosyncratic at this sample size.

Qualitative inspection of the three worst-performing bbox + best-slice cases (cases 14, 1, and 16; Dice = 0.67, 0.72, 0.78) reveals the dominant failure mode: SAM2 fills the bounding box as a near-rectangular blob, with the predicted boundary approximating the box geometry rather than the organ contour. This box-filling artefact is the direct consequence of absent salient image boundaries within the box — SAM2's mask decoder, pre-trained on natural images [1] where objects exhibit texture, colour, and edge gradients, defaults to filling the constrained region when no such cues are present. This is qualitatively consistent with Ji et al. [11], who observed that SAM produces poor results in scenes where the target blends with the background.

5. Discussion

The results converge on a single underlying cause for SAM2's limitations on CT liver segmentation: the near-uniform Hounsfield-unit appearance of liver parenchyma under standard windowing removes the texture and edge signals that SAM2's ViT encoder was pre-trained to exploit on natural images [1, 5]. This manifests in three experimentally distinct observations: point encoder saturation (RQ1), box-edge dependency (RQ2), and box-filling failure (RQ3).

On the oracle prompt upper bound. The strongest condition in our study — bbox + best slice, Dice 0.775 — represents a best-case ceiling for zero-shot SAM2 on this task, achievable only with perfect bounding-box prompts derived from ground truth. A realistic clinical workflow with automated detection or user-provided bounding boxes will produce lower Dice, likely in the range 0.60–0.72 based on the known degradation reported when oracle boxes are replaced with detector-predicted boxes in analogous SAM studies [9]. We emphasise this to avoid the overclaiming that has characterised some zero-shot foundation model evaluations: SAM2 out-of-the-box is not a viable clinical liver segmentation tool.

On zero-shot versus fine-tuned approaches. We do not claim that zero-shot SAM2 should be preferred for CT liver segmentation — on the contrary, our results reinforce the conclusion of Ma et al. [12], Wu et al. [13], and Cheng et al. [14] that domain adaptation is necessary. The value of this study is not in proposing SAM2 as a deployment solution, but in characterising precisely where and why zero-shot performance breaks down, providing a principled basis for deciding whether fine-tuning, prompt engineering, or windowing adaptation is the most efficient intervention.

On generalisability. The study uses five healthy liver-donor subjects scanned at portal venous phase. Results may not generalise to: (a) pathological livers with tumours, cirrhosis, or altered enhancement; (b) non-contrast or arterial phase acquisitions with different liver-background contrast; (c) other CT scanners or institutions with different noise characteristics; or (d) other abdominal organs with different size, shape, or texture properties. We explicitly caution against extrapolating these findings beyond the stated scope.

Limitations. n = 5 limits statistical power throughout; all results are exploratory. Oracle prompts impose an upper-bound bias that overestimates real-world performance [§3.3]. Evaluation is on single 2D slices; volumetric extension via SAM2's video propagation mode [1] is a natural future direction. All p-values are underpowered and should not be interpreted as confirmatory.

6. Conclusion

MedSeg-Eval provides a fast, fully reproducible, and agent-executable analysis of SAM2 on abdominal CT liver segmentation under oracle-prompt conditions, yielding three practically significant findings:

1. Bounding-box prompts are the only viable zero-shot strategy for CT liver under oracle conditions; all point-based strategies fail at a 100% rate even with perfect prompts.
2. Slice selection has a Dice impact of up to +0.40 for bbox but is negligible (≈+0.10) for point-based prompts — a prompt-dependent interaction not previously quantified for this task.
3. Grid-of-points prompting offers no benefit over a single centroid under oracle conditions, providing evidence that SAM2's point-prompt encoder saturates on homogeneous CT texture.

These findings establish an oracle-prompt performance ceiling for zero-shot SAM2 on CT liver segmentation. They do not constitute a claim that SAM2 is a viable deployment tool for this task without domain adaptation.

Reproducibility Statement

All results in this paper are fully reproducible by executing SKILL.md in order. Software dependencies are pinned with exact version numbers in requirements.txt, generated via pip freeze at execution time. The dataset is publicly archived at DOI 10.5281/zenodo.3431873 under CC-BY-SA 4.0. The random seed is fixed to 42 throughout. No manual interventions, parameter tuning, or case selection based on performance were performed at any stage of this study.

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