UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image

Zeen Zhou; Qiuhua Chen; Xiaojun Cao; Changmao Chen; Chao Sun; Bo Du

cs.CVAug 27, 2026

UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image

Authors: Zeen Zhou, Qiuhua Chen, Xiaojun Cao, Changmao Chen, Chao Sun, Bo Du

Organizations: Academy of Advanced Interdisciplinary Studies, Wuhan University, Wuhan, China · School of Computer Science, Wuhan University, Wuhan, China · Guangzhou Women and Children’s Medical Center, Guangzhou, China · School of Computer Science, National Engineering Research Center for Multimedia Software, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Institute of Artificial Intelligence, Wuhan University, Wuhan 430072, China

Abstract

Prenatal ultrasound examination is crucial for assessing fetal limb development and detecting congenital anomalies. However, existing artificial intelligence models often overlook fetal lethal skeletal dysplasias due to the lack of high-quality annotated data and a unified framework for multiple long bones. Moreover, generic segmentation models struggle with the inherent noise and semantic gaps in ultrasound images. To address these challenges, we construct the Fetal Limb Bones (FLB) dataset, comprising high-quality annotations for the humerus, femur, tibia-fibula, and radius-ulna. Furthermore, we propose UniFLM (United Segmentation and Measurement on Fetal Limb Ultrasonic Image), a unified framework for automatic cross-plane segmentation and measurement. UniFLM incorporates a Semantic Alignment Skip Connection (SASC) module to bridge the semantic gap between encoder and decoder features, and a Positive Sampling (PoSamp) strategy to filter noise and extract essential semantic information. Finally, a Point Regression Mapping (PRM) module is introduced to learn clinician annotation patterns for precise bone length measurement. Extensive experiments conducted on the FLB dataset and the public FetalP5 benchmark demonstrate that UniFLM achieves competitive performance with consistent generalization across four bone categories and external multi-center data, supported by comprehensive statistical validation including Bland-Altman agreement analysis and bootstrap confidence intervals. The source code is publicly available at https://github.com/chosen1203/UniFLM.

Figures & tables

Figure 1: The overall architecture of UniFLM. The backbone consists of a 6-stage Encoder ( $\mathbf{En}$ ) and a 5-stage Decoder ( $\mathbf{De}$ ). (1) SASC : Multi-scale encoder features $\mathbf{I}^{e}$ are projected and aligned via the centralized SASC module. (2) PoSamp : The Positive Sampling module at the bottleneck filters noise from feature $\mathbf{Z}$ to generate $\mathbf{Z}^{*}$ . (3) Segmentation & PRM : The decoder outputs an initial mask; initial points $\mathbf{P}_{ini}$ are extracted and fed into the PRM module to predict the final measurement points $\mathbf{P}_{pred}$ .

Figure 2: Detailed structure of the proposed modules. Left (SASC): Multi-scale features $\mathbf{I}^{e}_{i}$ are projected and tokenized, concatenated into $\mathbf{I}^{e}_{c}$ , then refined via Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) before re-projection to original resolutions. Right (PoSamp): The deepest feature $\mathbf{Z}$ is filtered by a learned gating mechanism to produce $\mathbf{Z}^{*}$ via residual connection.

Figure 3: Representative samples from the FLB dataset showing (a) Femur, (b) Humerus, (c) Forearm, and (d) Leg. Red contours denote expert annotations.

Figure 4: Distribution of samples across the four bone categories in the FLB dataset, showing gestational age distribution within each category.

Model	Femur		Humerus
Model	Dice (%)	IoU (%)	Dice (%)	IoU (%)
UNet [ 29 ]	$82.66_{\pm 4.21}$	$72.49_{\pm 5.11}$	$87.06_{\pm 3.87}$	$78.92_{\pm 4.45}$
UNet++ [ 42 ]	$88.56_{\pm 2.15}$	$80.88_{\pm 2.76}$	$89.43_{\pm 1.98}$	$81.61_{\pm 2.55}$
Attention U-Net [ 23 ]	$87.21_{\pm 2.54}$	$79.15_{\pm 3.12}$	$88.76_{\pm 2.31}$	$80.42_{\pm 2.95}$
SwinUNet [ 6 ]	$78.99_{\pm 5.42}$	$67.90_{\pm 6.78}$	$78.99_{\pm 5.01}$	$67.51_{\pm 6.12}$
TransUNet [ 10 ]	$87.14_{\pm 2.89}$	$78.96_{\pm 3.45}$	$89.47_{\pm 2.12}$	$81.63_{\pm 2.68}$

Table 1: Quantitative comparison on the FLB dataset (Mean

\pm

SD across test samples). Bold : best; Underline : second best.

Comparison	Femur	Humerus	Forearm	Leg
UniFLM vs. UNet	$<$ 0.001	$<$ 0.001	$<$ 0.001	$<$ 0.001
UniFLM vs. UNet++	0.087	0.124	0.002	0.012
UniFLM vs. SAM-US	0.035	0.041	$<$ 0.001	0.004
UniFLM vs. VM-UNet	0.156	0.203	0.028	0.033
UniFLM vs. U-KAN	0.092	0.118	0.015	0.041

Table 2: Statistical significance (

p

-values from paired

t

-tests). Values

<0.05

(bold) indicate significance at the 95% confidence level.

Module			Dice (%)
SASC	PoSamp	PRM	Femur	Humerus	Forearm	Leg
✗	✗	✗	$86.96_{\pm 0.52}$	$89.43_{\pm 0.45}$	$68.36_{\pm 0.78}$	$73.69_{\pm 0.65}$
✓	✗	✗	$87.82_{\pm 0.48}$	$89.36_{\pm 0.42}$	$67.02_{\pm 0.82}$	$73.05_{\pm 0.68}$
✗	✓	✗	$87.55_{\pm 0.50}$	$89.57_{\pm 0.40}$	$68.53_{\pm 0.75}$	$72.21_{\pm 0.70}$
✓	✓	✗	$88.70_{\pm 0.42}$	$90.06_{\pm 0.35}$	$69.55_{\pm 0.68}$	$73.80_{\pm 0.58}$
✓	✓	✓	$\mathbf{89.90}_{\pm 0.38}$	$\mathbf{90.76}_{\pm 0.32}$	$\mathbf{70.55}_{\pm 0.62}$	$\mathbf{74.50}_{\pm 0.52}$

Table 3: Ablation study on module effectiveness (Mean Dice

\pm

SD across 3 seeds).

Method	Femur	Humerus	Forearm	Leg
	MED / MAE	MED / MAE	MED / MAE	MED / MAE
Geometric (skeleton)	$4.21_{\pm 1.2}$ / $1.85_{\pm 0.5}$	$3.98_{\pm 1.1}$ / $1.72_{\pm 0.4}$	$6.54_{\pm 1.8}$ / $2.89_{\pm 0.7}$	$7.12_{\pm 2.0}$ / $3.15_{\pm 0.8}$
Geometric (ellipse)	$3.87_{\pm 1.0}$ / $1.68_{\pm 0.4}$	$3.65_{\pm 0.9}$ / $1.58_{\pm 0.4}$	$5.98_{\pm 1.6}$ / $2.64_{\pm 0.6}$	$6.45_{\pm 1.8}$ / $2.85_{\pm 0.7}$
Regression (direct)	$3.45_{\pm 0.9}$ / $1.52_{\pm 0.4}$	$3.21_{\pm 0.8}$ / $1.41_{\pm 0.3}$	$5.12_{\pm 1.4}$ / $2.26_{\pm 0.6}$	$5.78_{\pm 1.6}$ / $2.55_{\pm 0.6}$
PRM (ours)	$\mathbf{2.89}_{\pm 0.7}$ / $\mathbf{1.27}_{\pm 0.3}$	$\mathbf{2.76}_{\pm 0.6}$ / $\mathbf{1.21}_{\pm 0.3}$	$\mathbf{4.38}_{\pm 1.2}$ / $\mathbf{1.93}_{\pm 0.5}$	$\mathbf{4.91}_{\pm 1.4}$ / $\mathbf{2.17}_{\pm 0.5}$

Table 4: Impact of PRM on measurement accuracy (Mean

\pm

SD). MED: Mean Endpoint Distance (px), MAE: Mean Absolute Error (mm).

Figure 5: Cumulative Distribution Function (CDF) of measurement errors. The vertical dashed line indicates the clinically acceptable threshold of 2.0 mm.

Figure 6: Performance across gestational age groups. (a) Dice coefficient distribution. (b) Measurement error (MAE) distribution.

Figure 7: Robustness analysis under varying noise levels.

Model	Params (M)	GFLOPs	FPS	Dice (%)	MAE (mm)
UNet	34.5	65.4	120	76.82	2.40
UNet++	36.6	84.2	95	79.15	2.12
TransUNet	105.3	110.4	65	77.41	2.28
MedSAM	93.7	156.8	42	78.05	2.18
VM-UNet	42.1	78.5	88	79.88	2.05
UniFLM	47.2	72.1	102	81.43	1.65

Table 5: Computational efficiency comparison (Input:

256\times 256

, GPU: V100).

Comparison	Femur	Humerus	Forearm	Leg
Expert 1 vs. Expert 2	$1.10\pm 0.8$	$0.95\pm 0.6$	$1.45\pm 1.1$	$1.52\pm 1.2$
Expert 1 vs. Expert 3	$1.15\pm 0.9$	$1.02\pm 0.7$	$1.38\pm 1.0$	$1.48\pm 1.1$
Expert 2 vs. Expert 3	$1.08\pm 0.7$	$0.98\pm 0.6$	$1.42\pm 1.0$	$1.55\pm 1.2$
UniFLM vs. Expert 1	$1.25\pm 0.9$	$1.18\pm 0.7$	$1.85\pm 1.3$	$2.05\pm 1.4$
UniFLM vs. Expert 2	$1.28\pm 0.9$	$1.22\pm 0.8$	$1.90\pm 1.3$	$2.12\pm 1.5$
UniFLM vs. Expert 3	$1.22\pm 0.8$	$1.15\pm 0.7$	$1.82\pm 1.2$	$1.98\pm 1.4$

Table 6: Inter-observer variability analysis (measurement error in mm, Mean

\pm

SD).

Metric	FLB (Private)		FetalP5 (Public)
Metric	Mean	$\pm$ SD	Mean	$\pm$ SD
Dice (%)	88.39	8.76	85.11	12.82
mIoU (%)	79.92	9.58	75.64	14.25
MAE (px)	7.65	4.84	13.83	–
Bland-Altman Bias (px)	$-0.76$	–	–	–
95% LoA (px)	$[-25.0,+23.5]$	–	–	–

Table 7: Cross-dataset generalization: UniFLM performance on the public FetalP5 dataset (Femur plane,

n=809

) vs. the private FLB test set (

n=300

femurs).

Bone Category	Count	Dice (%)	mIoU (%)	MAE (px)	DPEDE (px)
Femur (FL)	100	$89.90\pm 6.5$	$82.19\pm 8.1$	$7.2\pm 4.5$	$22.5\pm 16.5$
Humerus (HL)	120	$90.76\pm 5.8$	$83.83\pm 7.5$	$6.8\pm 4.1$	$21.3\pm 15.8$
Forearm (RA+UL)	40	$70.55\pm 11.2$	$57.12\pm 13.8$	$9.5\pm 5.8$	$28.7\pm 20.1$
Leg (TI+FI)	40	$74.50\pm 10.5$	$62.35\pm 12.5$	$8.8\pm 5.2$	$26.4\pm 18.5$

Table 8: Per-bone performance analysis (Mean

\pm

SD).

Image	DPEDE	MAE	Dice	GT	Pred	Primary Cause
	(px)	(px)		(px)	(px)
10278	128.7	34.5	0.602	298.1	170.0	Severe underexposure, incomplete seg.
10282	48.1	16.0	0.915	390.9	415.3	Low image contrast
10256	43.6	15.0	0.880	468.0	464.3	Low contrast, underexposure

Table 9: Top-3 worst-performing cases ranked by DPEDE.

Figure 8: Qualitative comparison and Grad-CAM. UniFLM produces complete segmentations in shadowed regions where baselines fail.

Figure 9: Parameter sensitivity analysis. (a) Performance vs. loss weight

\lambda_{1}

. (b) Training loss curve showing stable convergence.

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Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the scarcity of pixel-level annotations. To address this issue, we propose DACL, a semi-supervised framework for robust fetal US image segmentation. DACL jointly trains a deployment-oriented lightweight convolutional network (1.47\thinsp\mathrm{M} parameters) and a Transformer-based network, leveraging labeled data for supervised learning and unlabeled data via CPS. To enhance prediction stability, we introduce a dual-agreement consistency loss that couples pixel-wise probabilistic divergence with entropy-guided confidence alignment. Unlike conventional CPS methods that enforce agreement only at the prediction level, DACL explicitly regularizes both distributional alignment and uncertainty, thereby suppressing unreliable pseudo-labels and enabling stable cross-architecture pseudo-label learning under extreme annotation scarcity. Furthermore, an interpolation-based consistency strategy using mixup is applied to unlabeled samples to enhance robustness. Under 5% labeled data, DACL improves Dice by up to 2.77% and reduces HD95 by up to 14.69 mm compared with the strongest recent semi-supervised methods, demonstrating significant improvements in boundary accuracy on both fetal head and abdomen datasets. These results demonstrate the effectiveness of agreement-based consistency learning for annotation-efficient fetal US segmentation. Our code is on GitHub.

Abstract

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