ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization

Md. Abdur Rahman; Md Noman Hossain; Arefin Ittesafun Abian; Mohaimenul Azam Khan Raiaan; Yan Zhang; Mirjam Jonkman; Sami Azam

cs.CVDec 27, 2025

ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization

Authors: Md. Abdur Rahman, Md Noman Hossain, Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan, Yan Zhang, Mirjam Jonkman, Sami Azam

Organizations: Applied Artificial Intelligence and Intelligent Systems (AAIINS) Laboratory, Dhaka 1217, Bangladesh · Department of Computer Science and Engineering, United International University, Dhaka, 1212, Bangladesh · Department of Data Science and Artificial Intelligence, Monash University, Clayton, VIC, 3153, Australia · Faculty of Science and Technology, Charles Darwin University, Darwin, 0909, Australia

Abstract

Gliomas are among the most aggressive cancers, with complex diagnostic processes. Existing glioma segmentation methods often struggle with high variability in imaging data and inadequate optimization. Furthermore, radiomic analysis is typically applied only after segmentation is finished, limiting its ability to inform the segmentation process itself. To address these challenges, we propose a novel radiomics-enhanced fused residual multiparametric 3D network (ReFRM3D) for brain tumor characterization. The framework is based on a 3D U-Net architecture and features multi-scale feature fusion, hybrid upsampling, and an extended residual skip mechanism. Additionally, we introduce a radiomic conditioning mechanism that extracts texture and intensity descriptors from an intermediate coarse segmentation and re-injects them into the decoder to refine the final output. Experimental results on BraTS2019, BraTS2020, and BraTS2021 show strong performance, with mean DSC values of 93.45%, 93.61%, and 92.06%, respectively, across whole tumor, enhancing tumor, and tumor core regions. Compared with recent models, ReFRM3D improves average DSC by 5.79%, 7.25%, and 0.96% on these datasets. Our model also generalized well on the BraTS-Africa dataset with an average DSC of 87.8%, despite differences in scanner field strength and patient demographics.

Figures & tables

Figure 1: Architectural comparison between existing frameworks and the proposed architecture. Top : Standard pipelines utilizing single-scale encoder-decoders, direct skip connections, and basic interpolation. Bottom : The proposed ReFRM3D framework incorporating PCA brain isolation, multi-scale encoding, extended residual skip connections, and a radiomic conditioning loop that extracts descriptors from a coarse mask to guide the final segmentation.

Figure 2: Overview of the proposed glioma segmentation framework. The expansion layers mirror the contraction layers in size and filters.

Figure 3: Visualization of histograms of original and Z-score normalized intensity distributions with intensity comparison across multiple MRI scans. The top-left panel shows the original intensity histogram with high variability (mean $\mu=270.66$ , standard deviation $\sigma=84.43$ ), while the top-right panel displays the normalized distribution centered at zero with unit variance. The bottom panel illustrates that while original mean intensities fluctuate significantly across scans (ranging from 290 to 550), the normalized values remain stable (70 to 110), which also ensures consistent input for model training.

Figure 4: Visualization of total slice ranges. Tumors consistently appear between slices 46 and 113 for all 369 volumes (BraTS2020). On average, they occupy 68 slices per volume. The top panel shows tumor slice ranges (gray lines) within the total volume range (light blue bars). Average start and end positions are marked by green and red dashed lines, respectively. The bottom panel displays tumor midpoint positions and shows anatomical consistency across patients, with an average center at slice 79.5. This analysis confirms that excluding non-tumor slices reduces the processed slice count by approximately 56% (from 155 to 68 slices) without affecting segmentation performance.

Figure 5: Overview of the Fused Multi-scale Feature Fusion (FMFF) module. Encoder features from multiple scales undergo channel alignment via $1\times 1\times 1$ convolutions and spatial alignment via adaptive pooling to form a unified representation.

Figure 6: Overview of the Hybrid Upsampling and Residual Integration (HURI) module. The decoder feature is upsampled using a learnable transposed convolution, integrated with the corresponding encoder feature via a residual connection, and refined through a final convolution block.

Figure 7: Epoch-wise accuracy and loss for training and validation

Figure 8: Qualitative random slice segmentation on a high-grade glioma (HGG) case. Rows show axial, coronal, and sagittal views; columns show T1ce, T2, and FLAIR modalities with overlaid contours, a contour-only ground-truth/prediction comparison, and an overlay. Solid lines denote ground truth and dashed lines denote model predictions, with WT, TC, and ET shown in green, red, and yellow, respectively.

Category	Parameter	Value
Preprocessing	Intensity threshold	99th percentile
	Margin cropping ( $\delta$ )	1.5 $\sigma$
	Input size	$128\times 128\times 128$
	Input channels	3 (FLAIR, T1ce, T2)
Training Config.	Optimizer	Adam
	Learning rate	0.0001

Table 1: Summary of hyperparameters and configuration settings used in ReFRM3D.

Figure 9: Visualization of a random slice of a low-grade glioma (LGG) case. Unlike the HGG case, the LGG lesions are more homogeneous with minimal enhancement.

	Modalities	WT	ET	TC	$m_{m}\,\text{DSC}(\%)$	WT	ET	TC	$m_{m}\,\text{JCS}(\%)$
	Modalities	DSC(%)			$m_{m}\,\text{DSC}(\%)$	JCS(%)			$m_{m}\,\text{JCS}(\%)$
BraTS2019	T1ce	92.98	91.17	92.54	92.23	86.88	83.77	85.59	85.41
	T2	95.05	94.00	94.71	94.59	90.57	88.68	89.73	89.66
	Flair	94.10	92.87	93.66	93.54	88.86	86.69	87.88	87.81
	avg.	94.04	92.68	93.64 $\rightarrow$	93.45	88.77	86.38	87.73 $\rightarrow$	87.63
BraTS2020	T1ce	93.30	90.25	92.74	92.09	87.44	82.23	85.38	85.02

Table 2: Segmentation results for different modalities. The columns

m_{m}\,\text{DSC}

and

m_{m}\,\text{JCS}

denote the m ean D ice S imilarity C oefficient and J accard C oefficient S imilarity for the respective m odalities.

Ref	WT	ET	TC	Avg.
Oktay et al. [ 41 ]	0.888	0.759	0.772	0.806
Wang et al. [ 42 ]	0.900	0.789	0.819	0.836
Valanarasu et al. [ 43 ]	0.876	0.732	0.739	0.782
Barzegar et al. [ 18 ]	0.901	0.890	0.887	0.893
Ullah et al. [ 25 ]	0.899	0.845	0.845	0.863
Li et al. [ 27 ]	0.890	0.835	0.842	0.856

Table 3: Comparison of segmentation performance on BraTS2019 dataset. The best and the second-best results are bolded and underlined , respectively.

Ref	WT	ET	TC	Avg.
Oktay et al. [ 41 ]	0.855	0.718	0.759	0.777
Qamar et al. [ 53 ]	0.874	0.794	0.837	0.835
Isensee et al. [ 28 ]	0.850	0.820	0.895	0.855
Wang et al. [ 42 ]	0.900	0.787	0.817	0.835
Xu et al. [ 20 ]	0.900	0.780	0.820	0.833
Jiang et al. [ 44 ]	0.890	0.773	0.803	0.822

Table 4: Comparison of segmentation performance on BraTS2020 dataset. The best and the second-best results are bolded and underlined , respectively.

Ref	WT	ET	TC	Avg.
Hatamizadeh et al. [ 57 ]	0.927	0.868	0.899	0.898
Isensee et al. [ 28 ]	–	–	–	0.912
Jiang et al. [ 44 ]	0.918	0.832	0.847	0.866
Li et al. [ 58 ]	0.923	0.858	0.863	0.881
Lin et al. [ 59 ]	0.933	0.885	0.901	0.906
Roy et al. [ 60 ]	–	–	–	0.915

Table 5: Comparison of segmentation performance on BraTS2021 dataset. The best and the second-best results are bolded and underlined , respectively.

Ref	Dataset	Acc.	Sen	Spec
[ 50 ]	BraTS19	–	75.84%	76.11%
[ 63 ]	BraTS19	98.90%	98.70%	–
[ 52 ]	BraTS19	99.09%	99.15%	99.98%
[ 64 ]	BraTS19-21	98.10%	99.05%	95.20%
[ 63 ]	BraTS20	99.60%	99.00%	–
[ 52 ]	BraTS20	99.88%	99.50%	99.97%

Table 6: Classification performance alongside recent methods on the BraTS benchmark, reported for context only. (–) denotes: values not given.

Model	BraTS	Dice (%)
Model	BraTS	WT	ET	TC
DDU-net [ 23 ]	2017	0.838	0.783	0.898
DenseNets [ 14 ]	2015	0.816	0.818	0.858
U-Net [ 15 ]	2015	0.733	0.726	0.890
	2017	0.763	0.642	0.876
C-ConvNet [ 26 ]	2018	0.872	0.911	0.920

Table 8: Comparison of Dice (%) for different datasets and methods. The best results are highlighted in bold, while the second-best results are underlined.

Method	ET	TC	WT	Avg.
nnU-Net	0.850	0.853	0.913	0.872
MedNeXt Ens.	0.878	0.876	0.924	0.893
BrainUNet	0.759	0.791	0.869	0.806
ReFRM3D (Ours)	0.843	0.880	0.911	0.878

Table 9: Comparison of segmentation performance on the BraTS-Africa dataset.

Metric	Mean $\pm$ Std	95% CI
ET	$0.843\pm 0.047$	$[0.832,\ 0.854]$
TC	$0.880\pm 0.035$	$[0.872,\ 0.888]$
WT	$0.911\pm 0.028$	$[0.905,\ 0.917]$
Avg	$0.878\pm 0.037$	$[0.869,\ 0.887]$

Table 10: Statistical analysis of ReFRM3D segmentation performance on the BraTS-Africa dataset (

n=146

). The 95% confidence interval (CI) was estimated from three independent runs to assess result variability.

Figure 10: Visualization of randomly selected slices for three tumor subtypes in the BraTS-Africa dataset.

Ref.	Model	Features	Params (M)	FLOPs (G)
[ 28 ]	nnUNet	CNN	19.07	412.65
[ 57 ]	SwinUNETR	Swin Transformer	61.98	394.84
[ 69 ]	TransUNet	Hybrid CNN‑Transformer	96.07	48.34
[ 70 ]	SegDiff	U‑Net + Diffusion	23	82.1
[ 45 ]	UNETR	Vision Transformer	92.58	41.19
[ 71 ]	MedSegDiff	U‑Net + Diffusion	25	1770

Table 11: Complexity comparison with the SOTA models. Our FLOPs are reported for the input size

128\times 128\times 128

Metric	$\sim$ Value
Total Params	8.64 M
Trainable Params	8.64 M
Non-trainable Params	4.9 K (BatchNorm stats)
FLOPs ( $128^{3}$ input)	263.54 GFLOPs
FLOPs per Param	30.49 K
Mem: weights (FP32, .h5)	68.0 MB

Table 12: System-level complexity of the proposed model.

Component	Parameters
Encoder (L1–L4)	880,288
Bottleneck + Context Pathway	4,426,768
FMFF (1×1 alignment convs)	22,000
Decoder L4 (HURI + rSkip + RadCond.)	2,491,792
Decoder L3 (HURI + rSkip + RadCond.)	623,744
Decoder L2 (HURI + rSkip + RadCond.)	156,352

Table 13: Parameter distribution by architectural component.

Com.	Exp.	Variant	DSC (%)			Mean	$\boldsymbol{\Delta}$ Mean
Com.	Exp.	Variant	WT	ET	TC	Mean	$\boldsymbol{\Delta}$ Mean
Fusion	F1	No fusion	76.3	72.1	74.8	74.40	0.00
	F2	Single-level fusion	78.5	74.3	76.9	76.57	$+$ 2.17
	F3	w/o scale alignment	80.1	76.2	78.7	78.33	$+$ 3.93
	F4	w/o context pathway	81.4	77.8	80.1	79.77	$+$ 5.37
	F5	+ Summation+1 $\times$ 1 (FMFF)	82.7	79.3	81.5	81.17	$+$ 6.77

Table 14: Component-wise ablation study on BraTS2021 (100 randomly selected samples from both HGG and LGG grades, trained for 50 epochs). Each component group is evaluated to isolate its individual contribution.

\Delta

Mean is relative to the baseline of each component group.

Dataset	LR	Loss	Training		Validation		T/E (s)
Dataset	LR	Loss	Accuracy	Loss	Accuracy	Loss	T/E (s)
BraTS19	0.01	D/F	0.915	0.232	0.921	0.181	110
	0.001	D/F	0.955	0.120	0.969	0.131	119
	0.0001	D&F	0.982	0.110	0.970	0.120	125
BraTS2020	0.01	D/F	0.939	0.200	0.939	0.171	108
	0.001	D/F	0.960	0.175	0.966	0.130	121

Table 15: Hyperparameter configurations and performance metrics. The columns LR and T/E (s) represent the L earning R ate and the average T ime per E poch in seconds, respectively. This setting uses the Adam optimizer with the input size of

128\times 128\times 128

Metric	LR	$128\times 128\times 128$		$256\times 256\times 256$
Metric	LR	Adam	SGD	Adam	SGD
Acc	0.01	0.959	0.886	0.981	0.917
	0.001	0.973	0.929	0.984	0.960
	0.0001	0.985	0.941	0.990	0.967
Loss	0.01	0.137	0.156	0.162	0.181
	0.001	0.128	0.143	0.136	0.167

Table 16: Controlled optimizer comparison on BraTS2021 (train) with fixed input size and learning rate.

Component	Variant	Tr/Acc	Tr/Loss	Avg DSC (%)	t(s)/Ep	GPU Mem	Inf/T(ms)
Slice Selection	Full range ∗	0.891	0.27	81.34	151±2.1	2,847 MB	312±8
	Tumor-only	0.929	0.21	83.89	123±2.4	2,413 MB	264±5
Normalization	No normalization ∗	0.891	0.27	81.34	151±2.1	2,847 MB	312±8
	Min-max	0.911	0.23	82.54	151±1.9	2,847 MB	312±7
	Z-score	0.929	0.20	83.12	151±1.7	2,847 MB	312±6
Brain Isolation	No isolation ∗	0.891	0.27	81.34	151±2.1	2,847 MB	312±8

Table 17: Preprocessing component ablation. GPU memory and inference time are reported for a single volume forward pass. Training time (per epoch) and inference time are reported in the t(s)/Ep and Inf/T(ms) columns, respectively. Baselines are indicated with ”

*

”.

Cropping	Acc	Loss	DSC (%)	t(s)/Ep
Axis-aligned	0.931	0.28	84.21	141
PCA (0.75 $\sigma$ )	0.938	0.23	84.89	108
PCA (1.00 $\sigma$ )	0.943	0.19	85.34	112
PCA (1.25 $\sigma$ )	0.944	0.17	85.73	119
PCA (1.50 $\sigma$ )	0.944	0.17	85.73	123
PCA (1.75 $\sigma$ )	0.941	0.21	80.89	129

Table 18: Sensitivity analysis of brain region cropping strategy and margin parameter on segmentation performance and training efficiency. Training time (per epoch) is reported in the t(s)/Ep column.

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Automatic brain tumor segmentation from multi-modal MRI remains challenging because volumetric models often incur substantial computational cost. This paper presents DALight-3D, a compact 3D U-Net variant that combines depthwise separable 3D convolutions, identifier-conditioned normalization, cross-slice attention, and adaptive skip fusion. The method is evaluated on the Medical Segmentation Decathlon Task01 BrainTumour benchmark under matched optimization settings against standard 3D U-Net, Attention U-Net, Residual 3D U-Net, and V-Net baselines. In the reported 50-epoch comparison, DALight-3D achieves a mean Dice of 0.727 with 2.22M parameters, compared with 0.710 Dice and 3.20M parameters for Residual 3D U-Net. Component-wise ablations show consistent performance degradation when SepConv, identifier-conditioned normalization, CSA, or SSFB is removed. These results indicate that DALight-3D offers a favorable accuracy-efficiency trade-off within the present benchmark setting.

Abstract

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