# Deep Learning and Multi-Modal MRI for the Segmentation of Sub-Acute and Chronic Stroke Lesions

Alessandro Di Matteo, Youwan Mahé, Stéphanie s Leplaideur, Isabelle Bonan, Elise Bannier, Francesca Galassi

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Alessandro Di Matteo, Youwan Mahé, Stéphanie s Leplaideur, Isabelle Bonan, Elise Bannier, et al. Deep Learning and Multi-Modal MRI for the Segmentation of Sub-Acute and Chronic Stroke Lesions. Pattern Recognition Letters, 2025, 199, pp.225-231. 10.1016/j.patrec.2025.11.017. hal-04647365v3

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# Deep learning and multi-modal MRI for the segmentation of sub-acute and chronic stroke lesions☆

Alessandro Di Matteo<sup>a, b, ∗</sup>, Youwan Mahé<sup>b, c</sup>, Stéphanie Leplaideur<sup>b, d, e, f</sup>, Isabelle Bonan<sup>b, d, e</sup>, Elise Bannier<sup>b, d</sup>, Francesca Galassi<sup>b, ∗</sup>

## ARTICLE INFO

Editor: Maria De Marsico

Dataset link: https://fcon_1000.projectsnitrc.org/indi/retro/atlas.html

**Keywords:**

Sub-acute stroke

Chronic stroke

MRI

Lesion segmentation

Deep learning

## ABSTRACT

Stroke is a leading cause of morbidity and mortality worldwide. Accurate segmentation of post-stroke lesions on MRI is crucial for assessing brain damage and informing rehabilitation. Manual segmentation, however, is time-consuming and prone to error, motivating the development of automated approaches. This study investigates how deep learning with multimodal MRI can improve automated lesion segmentation in sub-acute and chronic stroke. A single-modality baseline was trained on the public ATLAS v2.0 dataset (655 T1-w scans) using the nnU-Net v2 framework and evaluated on an independent clinical cohort (45 patients with paired T1-w and FLAIR MRI). On this internal dataset, we conducted a systematic ablation comparing (i) direct transfer of the ATLAS baseline, (ii) fine-tuning using T1-w only, and (iii) fusion of T1-w and FLAIR inputs through early, mid, and late fusion strategies, each tested with metric averaging and ensembling.

The ATLAS baseline model achieved a mean Dice score of 0.64 and a lesion-wise F1 score of 0.67. On the clinical dataset, ensembling improved performance (Dice 0.70 vs. 0.68; F1 0.79 vs. 0.73), while fine-tuning on T1-w data further increased accuracy (Dice 0.72; F1 0.78). The best overall results were obtained with a T1+FLAIR late-fusion ensemble (Dice 0.75; F1 0.80; Average Surface Distance (ASD) 2.94 mm), with statistically significant improvements, especially for small and medium lesions.

These results show that fine-tuning and multimodal fusion — particularly late fusion — improve generalization for post-stroke lesion segmentation, supporting robust, reproducible quantification in clinical settings.

## 1. Introduction

Stroke is a major global cause of morbidity and mortality, leading to severe and often long-term neurological impairments [1–5]. Magnetic Resonance Imaging (MRI) plays a central role in assessing brain damage after stroke. It is crucial both in the acute phase, immediately after onset, and in the sub-acute and chronic period occurring weeks or months later [6]. In the acute phase, rapid lesion identification is vital for guiding treatment and minimizing long-term disability, typically using diffusion-weighted and perfusion MRI. In later stages, structural MRI is preferred to evaluate residual damage and its impact on brain

structure and function [7,8]. T1-weighted (T1-w) images provide high-resolution anatomical detail essential for assessing structural damage, while Fluid-Attenuated Inversion Recovery (FLAIR) sequences, though not always acquired in clinical routine, can provide complementary information by highlighting lesion-related hyperintensities and subtle tissue changes that may not be visible on T1-w scans. Delineating stroke lesions on MRI remains challenging. Manual segmentation, while considered the reference standard, is labor-intensive, time-consuming, and prone to inter-rater variability. These limitations motivate the development of automated methods that can provide accurate and reproducible lesion delineations. Recent advances in deep learning have

E-mail addresses: alessandro.dimatteo1@graduate.univaq.it (A. Di Matteo), youwan.mahe@inria.fr (Y. Mahé), stephanie.leplaideur@irisa.fr (S. Leplaideur), isabelle.bonan@chu-rennes.fr (I. Bonan), elise.bannier@irisa.fr (E. Bannier), francesca.galassi@irisa.fr (F. Galassi).

https://doi.org/10.1016/j.patrec.2025.11.017

Received 30 June 2025; Received in revised form 13 October 2025; Accepted 6 November 2025

Available online 14 November 2025

0167-8655/© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote><sup>a</sup> DISIM, University of L'Aquila, L'Aquila, Italy</footnote>

<footnote><sup>b</sup> Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn, Rennes, France</footnote>

<footnote><sup>c</sup> Siemens Healthineers, Courbevoie, France</footnote>

<footnote><sup>d</sup> CHU Rennes, Hôpital Pontchaillou, Service de Médecine Physique et de Réadaptation (MPR) Adulte, Rennes, France</footnote>

<footnote><sup>e</sup> Inserm, Centre d'Investigation Clinique 1414, Rennes, France</footnote>

<footnote><sup>f</sup> Centre de Kerpape, Ploemeur, France</footnote>

<footnote>☆ This article is part of a Special issue entitled: 'HCBIA' published in Pattern Recognition Letters.</footnote>

<footnote>\* Corresponding authors.</footnote>
<caption>**Table 1**</caption>

Patient count and lesion volume statistics for each dataset. Lesion volumes are reported in milliliters (mL) as mean ± standard deviation, median, and maximum values.

<table><thead><tr><th>Dataset</th><th>Patients (n)</th><th>Mean (mL)</th><th>Median (mL)</th><th>Max (mL)</th></tr></thead><tbody><tr><td>ATLAS v2.0</td><td>655</td><td>3.28 × 10<sup>6</sup><br>± 6.09 × 10<sup>6</sup></td><td>2.5 × 10<sup>6</sup></td><td>4.97 × 10<sup>6</sup></td></tr><tr><td>Internal cohort</td><td>45</td><td>5.43 × 10<sup>6</sup><br>± 5.91 × 10<sup>6</sup></td><td>3.52 × 10<sup>6</sup></td><td>2.67 × 10<sup>6</sup></td></tr></tbody></table>

substantially improved medical image analysis [9–12], particularly in acute stroke segmentation [13–16]. However, automated segmentation of sub-acute and chronic lesions remains considerably underexplored. Late-stage lesions are often small, heterogeneous, and poorly contrasted, and clinical imaging protocols vary widely across scanners and sites. Moreover, the scarcity of publicly available annotated datasets has limited the development and validation of robust models. Accurate segmentation of sub-acute and chronic lesions is nevertheless critical for quantifying tissue loss, assessing functional impairment, and guiding rehabilitation strategies [17–19]. Reliable delineation enables objective tracking of lesion evolution over time, supporting prognostic assessment and evaluation of therapy outcomes. A recent review by Ahmed et al. [20] provides a comprehensive overview of automated post-stroke lesion segmentation approaches. Most of these studies rely on the ATLAS dataset [21], the first publicly available single-modality collection for sub-acute and chronic stroke. Its latest release, ATLAS v2.0, includes hundreds of T1-w MRI scans with expert lesion masks and serves as a benchmark for model training and evaluation.

Building on these foundations, this study investigates how fine-tuning and multimodal fusion can improve the segmentation of sub-acute and chronic stroke lesions. We first establish a single-modality nnU-Net v2 baseline on the public ATLAS v2.0 dataset (655 T1-w scans) using 5-fold cross-validation and evaluate its transferability to an independent clinical cohort (45 patients with paired T1-w and FLAIR MRI). On this internal dataset, we systematically compare T1-only and T1+FLAIR models across early, mid, and late fusion strategies, each assessed with metric averaging and ensembling under matched experimental conditions.

The main contributions of this work are threefold: (i) a reproducible evaluation framework that bridges public and clinical datasets for post-stroke lesion segmentation; (ii) one of the first systematic ablations of multimodal fusion strategies (early, mid, late) for sub-acute and chronic lesions; and (iii) a comprehensive clinical assessment including lesion-volume stratification.

Unlike previous works focused only on T1-w data [22,23], this study provides an in-depth analysis of how modality integration and fine-tuning affect performance.

## 2. Methods and materials

**Datasets.** Two datasets were used in this study: the public single-modality ATLAS v2.0 and an internal dual-modality clinical cohort. A summary of their characteristics is provided in Table 1, and lesion-volume distributions are shown in Fig. S1 (see Supplementary Material).

**ATLAS v2.0 (public, single modality).** The ATLAS v2.0 dataset [21] provides T1-w MRI scans with manually annotated stroke lesions. It includes 655 subjects for training, a public test set of 300, and a hidden test set of 316. All images are pre-aligned to the MNI-152 template and resampled to 1 mm isotropic resolution. The baseline single-modality model was trained and validated using five-fold cross-validation on the training set. Lesion volumes in this split averaged 3.28 × 10<sup>6</sup> mL (range 1.3 × 10<sup>-2</sup>–4.97 × 10<sup>6</sup> mL).

**Internal dual-modality clinical cohort.** The internal dataset originates from a multicentre clinical investigation (NCT01677091) conducted in compliance with national ethical and data-protection regulations. All participants provided written informed consent, and the relevant ethics committee approved the protocol. Patients were enrolled in either the sub-acute (7 days–6 months) or chronic (> 6 months) phase following ischemic or hemorrhagic stroke.

MRI data were acquired on Philips, Siemens, and GE scanners operating at 1.5 T or 3 T, including models such as Avanto, Skyra, Verio (Siemens); Achieva and Ingenia (Philips); and Optima, Signa HDx, and Discovery (GE). Each participant underwent paired T1-w and FLAIR acquisitions during the same session to ensure spatial correspondence. Lesions were manually segmented on FLAIR with T1-w guidance by a neuroimaging expert and verified by a neuroradiologist.

This cohort was used to fine-tune and evaluate the ATLAS-trained baseline. Models were first trained on T1-w images alone and then retrained on combined T1-w + FLAIR inputs to assess the contribution of multimodal fusion. The mean lesion volume was 5.43×10<sup>6</sup> ± 5.91×10<sup>6</sup> mL (median 3.52 × 10<sup>6</sup> mL; maximum 2.67 × 10<sup>6</sup> mL).

**Preprocessing** The preprocessing pipeline is built upon our previous framework [24,25], with the skull-stripping step based on Anima replaced by the HD-BET algorithm [26]. HD-BET was selected for its proven robustness across MRI vendors, field strengths, and acquisition protocols, making it particularly suitable for the heterogeneous multi-centre clinical dataset used in this study.

The pipeline, described here for T1-w images, comprised the following steps:

i. **Brain extraction:** Non-brain tissue was removed using HD-BET.

ii. **Bias correction:** Intensity non-uniformities were corrected using the N4 algorithm [27].

iii. **Registration:** Each T1-w image was rigidly registered to the MNI-152 template using animaPyramidalBMRegistration. Lesion masks, when available, were transformed using nearest-neighbor interpolation and refined using hole-filling to preserve their topology.

iv. **Reorientation:** All images and lesion masks were reoriented to the RAS (Right–Anterior–Superior) coordinate system for consistent alignment across subjects.

The same steps were applied to FLAIR images, except that each FLAIR was registered to its corresponding preprocessed T1-w scan from the same subject, which had already been aligned to MNI space. This ensured accurate within-subject correspondence between modalities while keeping interpolation minimal.

For the internal dataset, the full preprocessing pipeline was applied. For the ATLAS v2.0 dataset, which was already partially preprocessed, only HD-BET brain extraction and lesion refinement were performed. Small connected components with a volume < 2.5 × 10<sup>-2</sup> mL were removed to exclude microbleeds or interpolation artifacts, following clinical expert recommendations.

After these steps, nnU-Net v2 [10] automatically standardized voxel spacing and resampling parameters. The target spacing was set to the median per axis across the training set. Third-order spline interpolation was used for isotropic data. In contrast, for anisotropic volumes (maximum-to-minimum spacing ratio > 3), nearest-neighbor interpolation was applied along the low-resolution axis to prevent artifacts.

Segmentation masks were one-hot encoded into background and lesion classes before resampling. Each channel was linearly interpolated and then collapsed into a discrete mask via the argmax operation to avoid label smoothing. Nearest-neighbor interpolation was again used for anisotropic data.

Voxel intensities were normalized on a per-image basis using z-score normalization (subtracting the mean and dividing by the standard deviation) [28]. All preprocessed data were organized in a BIDS-like
directory structure [29] to ensure full traceability, reproducibility, and compatibility with downstream processing tools. Visual quality control was performed on all preprocessed images and masks to verify skull stripping, registration accuracy, and lesion-mask integrity.

**Model architectures** The segmentation pipeline was based on the nnU-Net v2 framework [10,11], using the 3D ResEncoder variant (Fig. 1). This architecture replaces standard convolutional blocks in the encoder with residual units to promote feature reuse and stabilize gradient flow. Each residual block consists of two consecutive Conv3d-InstanceNorm3d-LeakyReLU sequences with a residual connection before the final activation. The network comprises six resolution stages with 32, 64, 128, 256, and 320 feature maps, respectively. Downsampling is performed using strided convolutions at the start of each stage, while skip connections concatenate encoder and decoder features at matching resolutions. The decoder employs transposed convolutions for upsampling, followed by a Conv3d-InstanceNorm3d-LeakyReLU block. This configuration enables processing of 128 × 128 × 128 voxel patches with a 320-channel bottleneck representation at 4 × 4 × 4 spatial resolution. All models were trained with deep supervision using a combined soft Dice and cross-entropy loss.

The baseline single-modality model was trained on the ATLAS v2.0 dataset and subsequently fine-tuned on the internal dataset. To investigate the added value of multimodal information, separate dual-modality models were then trained using both T1-w and FLAIR inputs under three fusion schemes described below.

**Fusion strategies.** To integrate T1-w and FLAIR information, three fusion schemes were investigated (Fig. S2, Supplementary Material):

(i) **Early fusion:** T1-w and FLAIR volumes were concatenated as separate input channels to a single encoder-decoder model, enabling joint feature learning from the first convolutional layer. For stable optimization, the first-layer filters were initialized by duplicating the T1-w weights across both channels.

(ii) **Mid fusion:** Two modality-specific encoders processed T1-w and FLAIR separately, and their bottleneck features were concatenated and projected through a 1 × 1 × 1 convolution before entering a shared decoder. Several fusion mechanisms were tested — including feature summation, averaging, concatenation with projection, residual weighting, squeeze-excitation gating [30], and FiLM-based modulation — to assess modality interaction [31]. The concatenation + projection design yielded the most stable and accurate results, while more complex gating or modulation schemes tended to overfit and reduce generalization. Initialization of the FLAIR encoder with T1-w pretrained weights proved essential for stable convergence and consistent performance across lesion sizes.

(iii) **Late fusion:** Two independent encoder-decoder networks — one for each modality — were trained either separately or jointly, and their logit maps were fused voxel-wise to generate the final segmentation. Multiple fusion schemes were explored, including voxel-wise averaging, maximum, learnable scalar weighting, and learnable 1 × 1 × 1 convolutional fusion initialized as an equal-weight average. The learnable convolutional fusion achieved the best trade-off between stability and accuracy, outperforming both fixed and weighted alternatives. As in the mid-fusion setup, initializing the FLAIR branch with T1-w pretrained weights substantially improved convergence and final Dice and lesion-wise F1 scores compared with training both networks from scratch.

Only the best-performing configuration of each fusion strategy is reported in the manuscript, as these provided the most reproducible and representative results across experiments.

**Training and inference protocol** All models were trained under a unified nnU-Net v2 protocol to ensure comparability across experiments. Training was performed on 3D input patches of 128×128×128 voxels, randomly sampled from the resampled volumes. Optimization

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** ResEnc-nnU-Net architecture. The notation $O\times n$ indicates repeated blocks. Colored blocks highlight the type of fusion strategy applied (early, mid, or late) with the T1+FLAIR models.</figcaption></figure>

employed stochastic gradient descent (SGD) with polynomial learning-rate decay from an initial value of 0.01 and weight decay of $3\times10^{-5}$. Each epoch comprised 250 iterations (batch size = 2), and training was run for 1,700 epochs. The loss combined soft Dice and cross-entropy terms, with deep supervision applied at all decoder scales using nnU-Net's default decreasing weights. Dropout ($p = 0.2$) was applied in the encoder to reduce overfitting. Data augmentation included isotropic rescaling (scaling factor 0.85–1.25), random 3D rotations (±15°), and sagittal mirroring. Each fold was trained with a fixed random seed to ensure reproducibility.

Fine-tuning and fusion experiments adopted the exact sampling, augmentation, and optimization settings as the baseline. All networks were initialized from the pretrained ATLAS baseline and fine-tuned for 200 epochs using SGD with the same parameters but a reduced learning rate of $10^{-5}$. The training and validation splits were identical across all configurations to ensure a fair comparison. For each fold, the model checkpoint achieving the highest validation Dice score was selected for testing.

Baseline training on ATLAS v2.0 was performed on NVIDIA A40 and A100 GPUs, whereas fine-tuning and fusion experiments were run on NVIDIA RTX A5000 GPUs.

Inference used a sliding-window strategy with 50% overlap and Gaussian importance weighting. Test-time augmentation (TTA) was applied by mirroring the inputs along all three axes, and the logits from the augmented predictions were averaged before the final softmax activation. Post-processing removed isolated false positives using 3D connected-component analysis (minimum lesion size = 0.025 mL, consistent with preprocessing) and filled small intra-lesional holes. For anisotropic volumes, morphological operations were constrained to the high-resolution planes to mitigate through-plane artifacts.

Results were reported as mean metric values across the five folds. In addition, ensemble predictions were computed by averaging per-voxel logits from models trained with the same fold index before evaluating final performance.

**Evaluation metrics** Model performance was assessed using a combination of voxel-wise and lesion-wise metrics to provide a comprehensive evaluation and facilitate comparison with existing methods [20]. Voxel-wise metrics included precision, sensitivity, Dice score, and Jaccard index [32], as well as the Average Surface Distance (ASD), defined as the mean boundary distance between predicted and reference lesion masks. Lesion detection performance was quantified using the lesion-wise F1-score, where a predicted lesion was considered correctly detected if its connected voxels overlapped a reference lesion by at least 10%. The F1-score-1v metric, based on at least one shared voxel between prediction and reference, was also reported. Results were stratified by lesion volume — small (≤15 mL), medium (15–70 mL), and large (>70 mL) — following established stroke lesion size definitions [33]. Statistical significance of performance differences was
assessed using the Wilcoxon signed-rank test, with *p* < 0.05 considered significant.

**Ablation study and comparative analysis** We conducted a systematic ablation study to quantify the contributions of key components — imaging modality, fusion strategy, and prediction aggregation — to segmentation performance. All experiments were performed under identical training, inference, and evaluation conditions to ensure fair and controlled comparison.

For fine-tuning experiments on the internal dataset (Stages 2 and 3), each ATLAS fold served as initialization for a new five-fold cross-validation, yielding five fine-tuned models per fold and a total of 25 models per configuration. Training and validation splits were kept identical across all configurations to maintain strict comparability.

The ablation comprised three sequential stages:

*Stage 1 (Baseline transfer)* evaluated direct model transfer by applying ATLAS models to the internal dataset without fine-tuning.

*Stage 2 (T1-only fine-tuning)* fine-tuned the ATLAS models on internal T1-w data, performing a nested five-by-five cross-validation to obtain 25 models.

*Stage 3 (Dual-modality fusion)* testing three fusion strategies — early, mid, and late fusion — each following the same cross-validation procedure, yielding 25 models per strategy (75 in total).

All configurations were evaluated using two aggregation schemes: (i) ensembling, average per-voxel logits from models trained with the same fold index (pre-softmax); and (ii) metric averaging, average metrics across folds.

This framework enables a direct assessment of how fine-tuning, multimodal fusion, and prediction aggregation affect performance while holding all other factors constant.

## 3. Results

**ATLAS v2.0 cross-validation.** Table 2 summarizes the performance of the baseline model trained on the ATLAS v2.0 dataset using five-fold cross-validation, reporting mean, standard deviation, and median values. The baseline achieved a mean Dice score of 0.64. Voxel-wise precision and recall were 0.71 and 0.66, while lesion-wise F1 and F1-1v averaged 0.67 and 0.69, respectively. The Jaccard index was 0.53, and the ASD was 9.60 mm, reflecting good boundary alignment. Performance was consistent across folds, with low-to-moderate variability. Compared with prior work on ATLAS v2.0, our model achieved comparable or better results: Verma et al. reported Dice 0.65 and ASD 12.04 mm [22], while Huo et al. reported Dice 0.67 and F1-1v 0.56 [23].

Performance stratified by lesion volume (Table 3) showed a clear size-dependent trend. For small lesions (mean volume ≈3.28 mL, *n* = 423), Dice was 0.55 and lesion-wise F1/F1-1v were 0.62/0.65. For medium lesions (≈38.6 mL, *n* = 129), Dice increased to 0.77 and F1/F1-1v to 0.74/0.73. For large lesions (≈152.6 mL, *n* = 101), Dice reached 0.88 and F1/F1-1v 0.82/0.80, indicating excellent overlap. These results indicate that segmentation accuracy improves with lesion size.

**Internal clinical cohort.** Table 4 summarizes the voxel- and lesion-wise performance metrics (mean ± SD) of all models evaluated on the internal clinical cohort.

The directly transferred baseline model achieved a mean Dice score of 0.68, a precision of 0.78, a recall of 0.66, and a Jaccard of 0.55. Lesion-wise metrics indicated moderate detection performance (F1/F1-1v = 0.73/0.72) and an ASD of 6.95 mm. Applying ensemble averaging across folds consistently improved all metrics, increasing Dice to 0.70, Jaccard to 0.57, and F1/F1-1v to 0.79/0.76, while reducing ASD to 3.47 mm. Fine-tuning on internal T1-w data (Stage 2) further improved performance, yielding Dice 0.71 (precision 0.71, recall 0.76, Jaccard 0.58, F1/F1-1v 0.76/0.79) with ASD 7.19 mm. The assembled T1 fine-tuned model achieved its best single-modality performance, with a Dice score of 0.72, Jaccard 0.60, F1/F1-1v 0.78/0.82, and ASD 3.37 mm.

<caption>**Table 2**</caption>

Performance metrics across five folds on the ATLAS v2.0 dataset. Each cell reports the mean value, followed by the standard deviation in parentheses, and the median value.

<table><thead><tr><th>Fold</th><th>Recall</th><th>Precision</th><th>Dice</th><th>Jaccard</th><th>ASD (mm)</th><th>F1-score</th><th>F1-score-1v</th></tr></thead><tbody><tr><td rowspan="2">0</td><td>0.68<br>(0.30)</td><td>0.69<br>(0.28)</td><td>0.64<br>(0.28)</td><td>0.53<br>(0.26)</td><td>9.13<br>(15.04)</td><td>0.66<br>(0.32)</td><td>0.69<br>(0.29)</td></tr><tr><td>0.81</td><td>0.79</td><td>0.75</td><td>0.60</td><td>2.39</td><td>0.67</td><td>0.67</td></tr><tr><td rowspan="2">1</td><td>0.62<br>(0.29)</td><td>0.75<br>(0.26)</td><td>0.63<br>(0.28)</td><td>0.52<br>(0.27)</td><td>11.18<br>(16.41)</td><td>0.66<br>(0.30)</td><td>0.67<br>(0.27)</td></tr><tr><td>0.75</td><td>0.85</td><td>0.73</td><td>0.57</td><td>2.88</td><td>0.67</td><td>0.67</td></tr><tr><td rowspan="2">2</td><td>0.63<br>(0.33)</td><td>0.66<br>(0.33)</td><td>0.60<br>(0.32)</td><td>0.50<br>(0.29)</td><td>11.43<br>(18.50)</td><td>0.65<br>(0.37)</td><td>0.66<br>(0.34)</td></tr><tr><td>0.77</td><td>0.79</td><td>0.72</td><td>0.56</td><td>2.60</td><td>0.67</td><td>0.67</td></tr><tr><td rowspan="2">3</td><td>0.69<br>(0.27)</td><td>0.74<br>(0.28)</td><td>0.68<br>(0.25)</td><td>0.56<br>(0.25)</td><td>8.05<br>(15.46)</td><td>0.69<br>(0.31)</td><td>0.72<br>(0.28)</td></tr><tr><td>0.78</td><td>0.85</td><td>0.76</td><td>0.61</td><td>2.07</td><td>0.67</td><td>0.67</td></tr><tr><td rowspan="2">4</td><td>0.69<br>(0.29)</td><td>0.72<br>(0.26)</td><td>0.66<br>(0.26)</td><td>0.54<br>(0.25)</td><td>7.78<br>(13.97)</td><td>0.71<br>(0.29)</td><td>0.72<br>(0.28)</td></tr><tr><td>0.80</td><td>0.83</td><td>0.75</td><td>0.60</td><td>2.22</td><td>0.67</td><td>0.67</td></tr><tr><td>Mean</td><td>0.66</td><td>0.71</td><td>0.64</td><td>0.53</td><td>9.60</td><td>0.67</td><td>0.69</td></tr></tbody></table>

Extending to dual-modality (Stage 3) further improved segmentation accuracy. Among the fusion strategies, early fusion (joint input concatenation) achieved a Dice 0.70 and F1/F1-1v 0.75/0.78 (ASD 6.46 mm). The assembled early-fusion model improved Dice to 0.74, Jaccard to 0.62, F1/F1-1v to 0.79/0.81, and ASD to 3.14 mm. Mid-fusion models (dual encoders merged at the bottleneck) performed slightly lower, with Dice 0.68-0.69, F1 0.74-0.77, and ASD 8.16-7.68 mm for individual and assembled models, respectively. Late fusion, integrating modality-specific predictions at the output level, yielded the highest overall accuracy. The late-fusion model averaged reached Dice 0.73, F1/F1-1v 0.77/0.79, and ASD 5.05 mm, while the assembled configuration achieved the best performance across all metrics: Dice 0.75, Jaccard 0.63, F1/F1-1v 0.80/0.81, and the lowest ASD, 2.94 mm.

A stratified analysis by lesion size (Table 3) confirmed these trends. For small lesions (mean ≈ 4.96 mL, *n* = 14), the T1-FLAIR late-fusion ensemble achieved the highest Dice (0.65) and F1-1v (0.83), while the baseline ensemble reached the best F1 (0.78). For medium lesions (≈ 39.65 mL, *n* = 18), the T1-FLAIR late-fusion ensemble achieved the highest Dice (0.77) and F1-1v (0.85), and the T1 ensemble achieved the highest F1 (0.86). For large lesions (≈ 134.98 mL, *n* = 13), the T1-FLAIR late-fusion ensemble achieved the highest Dice (0.83), while the T1 ensemble achieved the highest F1/F1-1v (0.83/0.81).

Statistical validation was also performed using paired, two-sided Wilcoxon signed-rank tests at the subject level (*n* = 45) for voxel-wise Dice and lesion-wise F1, both overall and stratified by lesion size (Tables S1–8, see Supplementary). Ensembling significantly improved performance over single models: the ATLAS ensemble exceeded the single ATLAS baseline for Dice (*p* < 10⁻⁶) and F1 (*p* = 0.0004), with the most significant gains for small and medium lesions. Fine-tuned T1 models also yielded significant gains over the baseline (Dice *p* = 0.0116; F1 *p* = 0.0102), further enhanced by ensembling (Dice *p* ≈ 7.3×10⁻⁷; F1 *p* = 0.0080). Among multimodal configurations, early-fusion ensembles achieved significantly higher Dice (*p* ≈ 4.4×10⁻⁶) and F1 (*p* = 0.0385), while mid-fusion models provided selective improvements for medium lesions (*p* = 0.0123). Late fusion achieved the most substantial and most robust gains, significantly outperforming all other baselines for both Dice (overall *p* < 10⁻⁶; small *p* = 0.0054; medium *p* = 2.0×10⁻⁴; large *p* = 0.0164) and lesion-wise F1 (overall *p* = 0.0013).

Overall, the combined quantitative and statistical analyses demonstrate that (1) fine-tuning substantially improves performance relative to direct transfer, (2) ensembling consistently enhances both voxel- and lesion-level accuracy, and (3) multimodal integration — particularly via late fusion — provides the most robust and spatially precise segmentation across lesion sizes. Qualitative examples are provided in Figures S3-4 (see Supplementary Material) for visual comparison.
<caption>**Table 3**</caption>

Stratified performance by lesion volume. Entries report mean values. Single (*) and double asterisks (**) denote the best and second-best performing models, respectively.

<table><caption>**Table 4**</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Dataset</th><th colspan="3">Small (≤ 15 mL)</th><th colspan="3">Medium (15–70 mL)</th><th colspan="3">Large (> 70 mL))</th></tr><tr><th>Dice</th><th>F1-score</th><th>F1-score-1v</th><th>Dice</th><th>F1-score</th><th>F1-score-1v</th><th>Dice</th><th>F1-score</th><th>F1-score-1v</th></tr></thead><tbody><tr><td>Baseline</td><td>ATLAS v2.0</td><td>0.55</td><td>0.62</td><td>0.65</td><td><strong>0.77**</strong></td><td>0.74</td><td>0.73</td><td><strong>0.88*</strong></td><td><strong>0.82**</strong></td><td><strong>0.80**</strong></td></tr><tr><td>Baseline</td><td>Internal</td><td>0.57</td><td>0.73</td><td>0.75</td><td>0.70</td><td>0.74</td><td>0.73</td><td>0.78</td><td>0.72</td><td>0.66</td></tr><tr><td>Baseline ensemble</td><td>Internal</td><td><strong>0.60**</strong></td><td><strong>0.78**</strong></td><td>0.78</td><td>0.71</td><td>0.79</td><td>0.78</td><td>0.79</td><td>0.78</td><td>0.70</td></tr><tr><td>T1fine-tuned</td><td>Internal</td><td>0.55</td><td>0.65</td><td>0.79</td><td>0.75</td><td><strong>0.83**</strong></td><td>0.82</td><td>0.81</td><td>0.80</td><td>0.77</td></tr><tr><td>T1 fine-tuned ensemble</td><td>Internal</td><td>0.58</td><td>0.65</td><td>0.80</td><td>0.76</td><td><strong>0.86*</strong></td><td><strong>0.84**</strong></td><td>0.82</td><td><strong>0.83*</strong></td><td><strong>0.81*</strong></td></tr><tr><td>T1+FLAIR early fusion</td><td>Internal</td><td>0.52</td><td>0.61</td><td>0.75</td><td><strong>0.77**</strong></td><td><strong>0.83**</strong></td><td>0.84</td><td>0.82</td><td>0.80</td><td>0.74</td></tr><tr><td>T1+FLAIR early fusion ensemble</td><td>Internal</td><td>0.59</td><td>0.77</td><td><strong>0.83*</strong></td><td><strong>0.78*</strong></td><td>0.80</td><td><strong>0.85*</strong></td><td>0.82</td><td>0.80</td><td>0.75</td></tr><tr><td>T1+FLAIR mid fusion</td><td>Internal</td><td>0.46</td><td>0.60</td><td>0.71</td><td>0.76</td><td>0.82</td><td><strong>0.85*</strong></td><td>0.81</td><td>0.77</td><td>0.75</td></tr><tr><td>T1+FLAIR mid fusion ensemble</td><td>Internal</td><td>0.48</td><td>0.70</td><td>0.72</td><td><strong>0.77**</strong></td><td><strong>0.83**</strong></td><td><strong>0.85*</strong></td><td>0.82</td><td>0.79</td><td>0.76</td></tr><tr><td>T1+FLAIR late fusion</td><td>Internal</td><td><strong>0.60**</strong></td><td>0.72</td><td>0.75</td><td><strong>0.77**</strong></td><td>0.80</td><td>0.82</td><td>0.82</td><td>0.77</td><td>0.77</td></tr><tr><td>T1+FLAIR late fusion ensemble</td><td>Internal</td><td><strong>0.65*</strong></td><td><strong>0.81*</strong></td><td><strong>0.82**</strong></td><td><strong>0.77**</strong></td><td>0.80</td><td><strong>0.85*</strong></td><td><strong>0.83**</strong></td><td>0.77</td><td>0.73</td></tr></tbody></table>

Performance metrics on the Internal dataset. Metrics are reported as mean (standard deviation) and median. Single (*) and double asterisks (**) denote the best and second-best performing models, respectively.

<table><thead><tr><th>Model</th><th>Recall</th><th>Precision</th><th>Dice</th><th>Jaccard</th><th>ASD</th><th>F1-score</th><th>F1-score-1v</th></tr></thead><tbody><tr><td rowspan="3">Baseline</td><td>0.66<br>(0.26)</td><td><strong>0.78**</strong></td><td>0.68<br>(0.21)</td><td>0.55<br>(0.21)</td><td>6.95<br>(15.25)</td><td>0.73<br>(0.26)</td><td>0.72<br>(0.26)</td></tr><tr><td>0.73</td><td>0.86</td><td>0.72</td><td>0.56</td><td>2.49</td><td>0.73</td><td>0.73</td></tr><tr><td>0.67<br>(0.26)</td><td><strong>0.80*</strong></td><td>0.70<br>(0.21)</td><td>0.57<br>(0.21)</td><td>3.47<br>(3.09)</td><td><strong>0.79**</strong></td><td>0.76<br>(0.27)</td></tr><tr><td rowspan="2">Baseline ensemble</td><td>0.74</td><td>0.88</td><td>0.76</td><td>0.61</td><td>2.41</td><td>1.0</td><td>0.80</td></tr><tr><td><strong>0.76**</strong></td><td>0.71<br>(0.25)</td><td>0.71<br>(0.21)</td><td>0.58<br>(0.21)</td><td>7.19<br>(15.58)</td><td>0.76<br>(0.29)</td><td>0.79<br>(0.25)</td></tr><tr><td rowspan="2">T1 fine-tuned</td><td>0.85</td><td>0.78</td><td>0.78</td><td>0.64</td><td>2.58</td><td>0.88</td><td>0.88</td></tr><tr><td><strong>0.77*</strong></td><td>0.73<br>(0.24)</td><td>0.72<br>(0.21)</td><td>0.60<br>(0.21)</td><td>3.37<br>(3.09)</td><td>0.78<br>(0.31)</td><td><strong>0.82*</strong><br>(0.27)</td></tr><tr><td rowspan="2">T1 fine-tuned ensemble</td><td>0.86</td><td>0.80</td><td>0.79</td><td>0.65</td><td>2.33</td><td>1.0</td><td>1.0</td></tr><tr><td>0.73<br>(0.24)</td><td>0.74<br>(0.24)</td><td>0.70<br>(0.24)</td><td>0.59<br>(0.23)</td><td>6.46<br>(14.14)</td><td>0.75<br>(0.30)</td><td>0.78<br>(0.25)</td></tr><tr><td rowspan="2">T1+FLAIR early fusion</td><td>0.80</td><td>0.80</td><td>0.79</td><td>0.65</td><td>2.17</td><td>0.87</td><td>0.87</td></tr><tr><td>0.74<br>(0.24)</td><td>0.77<br>(0.24)</td><td><strong>0.74*</strong><br>(0.23)</td><td><strong>0.62**</strong><br>(0.22)</td><td><strong>3.14**</strong><br>(4.02)</td><td><strong>0.79**</strong><br>(0.30)</td><td><strong>0.81**</strong><br>(0.26)</td></tr><tr><td rowspan="2">T1+FLAIR early fusion ensemble</td><td>0.82</td><td>0.83</td><td>0.82</td><td>0.70</td><td>1.72</td><td>1.0</td><td>1.0</td></tr><tr><td>0.70<br>(0.27)</td><td>0.71<br>(0.26)</td><td>0.68<br>(0.25)</td><td>0.56<br>(0.24)</td><td>8.16<br>(15.81)</td><td>0.74<br>(0.29)</td><td>0.77<br>(0.27)</td></tr><tr><td rowspan="2">T1+FLAIR mid fusion</td><td>0.79</td><td>0.80</td><td>0.78</td><td>0.64</td><td>2.68</td><td>0.87</td><td>0.9</td></tr><tr><td>0.70<br>(0.28)</td><td>0.74<br>(0.26)</td><td>0.69<br>(0.26)</td><td>0.58<br>(0.24)</td><td>7.68<br>(16.39)</td><td>0.77<br>(0.32)</td><td>0.78<br>(0.31)</td></tr><tr><td rowspan="2">T1+FLAIR mid fusion ensemble</td><td>0.80</td><td>0.82</td><td>0.80</td><td>0.67</td><td>2.33</td><td>1.0</td><td>1.0</td></tr><tr><td>0.75<br>(0.24)</td><td>0.76<br>(0.23)</td><td>0.73<br>(0.20)</td><td>0.61<br>(0.20)</td><td>5.05<br>(10.25)</td><td>0.77<br>(0.27)</td><td>0.79<br>(0.26)</td></tr><tr><td rowspan="2">T1+FLAIR late fusion</td><td>0.84</td><td>0.84</td><td>0.80</td><td>0.67</td><td>2.21</td><td>0.88</td><td>0.87</td></tr><tr><td><strong>0.76**</strong><br>(0.24)</td><td><strong>0.78**</strong><br>(0.22)</td><td><strong>0.75*</strong><br>(0.20)</td><td><strong>0.63*</strong><br>(0.20)</td><td><strong>2.94*</strong><br>(3.30)</td><td><strong>0.80*</strong><br>(0.29)</td><td><strong>0.81**</strong><br>(0.29)</td></tr><tr><td rowspan="2">T1+FLAIR late fusion ensemble</td><td>0.85</td><td>0.85</td><td>0.81</td><td>0.67</td><td>1.81</td><td>1.0</td><td>1.0</td></tr></tbody></table>

## 4. Discussion

This study presents a reproducible deep learning framework for segmenting sub-acute and chronic stroke lesions using single-modality

(T1-weighted) and dual-modality (T1-weighted + FLAIR) MRI. By systematically integrating fine-tuning, multimodal fusion, and model ensembling within the nnU-Net v2 architecture, we demonstrated how these components jointly improve segmentation accuracy and generalization across datasets and lesion sizes. All trained models and accompanying scripts are publicly released to ensure transparency and reproducibility (https://github.com/alessandrodimatteo97/MultimodalityPostStrokeNet).

**Fine-tuning improves domain transfer.** Models trained on the public ATLAS v2.0 dataset generalized reasonably well to our independent clinical cohort (Dice = 0.68). However, fine-tuning on local data yielded clear gains across both voxel- and lesion-level metrics (Dice = 0.72; F1 = 0.78), confirming that adapting pretrained networks to site-specific data helps overcome differences in scanners and acquisition protocols. This finding is consistent with previous neuroimaging studies showing that fine-tuning substantially improves model robustness when transferring between domains with differing intensity profiles or lesion characteristics.

**Multimodal MRI provides complementary information.** The addition of FLAIR images further improved segmentation performance. Among the evaluated fusion strategies, late fusion achieved the best overall accuracy (Dice = 0.75; F1 = 0.80; ASD = 2.94 mm), outperforming both early- and mid-fusion approaches. These results indicate that maintaining separate feature extraction for each modality before integration helps preserve complementary information—fine anatomical detail from T1-w images and lesion-related hyperintensities from FLAIR. This observation agrees with established neuroimaging evidence showing that FLAIR is particularly sensitive to chronic tissue damage, whereas T1-w imaging primarily captures structural integrity. Early fusion still provided strong results (Dice = 0.74; F1 = 0.79), suggesting that direct input concatenation remains effective when inter-modality registration is accurate.

**Ensembling improves performances.** Across all configurations, ensembling consistently improved Dice, F1, and boundary accuracy. Averaging probability maps across models produced smoother, more spatially coherent lesion maps, particularly for small and medium lesions. This improvement aligns with the well-established stabilizing effect of ensemble averaging, which reduces the influence of random
initialization and local overfitting, leading to more reproducible and
reliable predictions.

**Lesion size remains a key challenge.** Despite the overall performance gains, segmentation accuracy remained dependent on lesion volume. Large lesions were delineated with high precision (Dice ≈ 0.83), whereas smaller lesions were more difficult to detect (Dice ≈ 0.65), likely due to partial-volume effects and lower contrast. Fine-tuning and the inclusion of FLAIR partially mitigated this issue, but small-lesion detection remains an open problem. Future work may explore specialized loss functions, adaptive sampling, or uncertainty weighting to focus learning on low-volume lesions.

**Patient-level variability.** Examining results at the individual-subject level provided further insight into model behavior and potential root causes of segmentation failures. Patients with large, well-defined lesions generally showed stable performance across all configurations, while small or low-contrast lesions accounted for most segmentation errors. In several cases, early fusion underperformed when minor T1-FLAIR misalignment or heterogeneous lesion intensity introduced conflicting signals, whereas late fusion remained more stable by maintaining independent modality representations before integration. Conversely, patients with strong and spatially consistent FLAIR hyperintensities benefited most from early fusion, which could exploit joint input features directly. Some apparent model errors were also attributable to data-related factors such as slight annotation inconsistencies or anisotropic resolution, particularly for small or low-contrast lesions. These observations highlight that the optimal fusion strategy may depend on lesion appearance, inter-modality alignment, and data quality, suggesting that future work should explore adaptive or confidence-weighted fusion mechanisms. Representative qualitative examples are shown in Figures S3-S4, and detailed per-patient analyses in Figures S5-S17 (see Supplementary Material).

**Comparison with previous works.** On the ATLAS v2.0 benchmark, our baseline model achieved performance comparable to or better than existing methods: lower surface distance (ASD = 9.6 mm vs. 12 mm in Verma et al.), higher lesion-wise F1-score (0.69 vs. 0.56 in Huo et al.), and similar Dice values (0.64 vs. 0.65-0.67) [22,23]. These improvements likely stem from the standardized preprocessing adopted in our pipeline — HD-BET skull stripping, N4 bias-field correction, and intensity normalization — which were inconsistently applied in earlier studies.

**Combined insights.** Taken together, the results demonstrate that each methodological component contributed complementary strengths. Fine-tuning enhanced cross-domain adaptation, ensembling improved prediction stability and boundary coherence, and multimodal fusion enriched tissue contrast and contextual information. Among the tested fusion strategies, late fusion proved most effective, suggesting that preserving modality-specific encoders before integration better retains complementary cues from T1-weighted and FLAIR MRI. A representative example in Figure S3 (see Supplementary Material) illustrates these effects: the dual-modality ensemble achieved a Dice score of 0.81 compared to 0.60 for the single-modality model, reflecting more precise boundary delineation when FLAIR information was incorporated. Figure S4 (see Supplementary Material) further highlights the differences between the three fusion strategies, with Dice scores of 0.69, 0.41, and 0.76 for early, mid, and late fusion, respectively. Overall, early and late fusion remained competitive when modalities were well aligned, whereas mid-fusion offered limited benefits, likely due to partial feature mixing at the bottleneck stage.

**Limitations and future directions.** This study has several limitations. First, the internal dataset used for fine-tuning and evaluation, though diverse, was modest in size (45 subjects). Larger, multi-site datasets will be essential to confirm generalizability and assess robustness across scanners. Second, segmentation accuracy for small or low-contrast lesions remains limited, emphasizing the need for architectures and loss formulations tailored to class imbalance. Finally, minor

T1-FLAIR misalignments occasionally degraded early-fusion performance, highlighting the importance of precise multimodal registration. Future work could extend the framework to diffusion or perfusion MRI and explore uncertainty-aware fusion methods to improve reliability in clinical deployment.

## 5. Conclusion

This study presented a reproducible deep learning framework for segmenting sub-acute and chronic stroke lesions from MRI, integrating fine-tuning, multimodal fusion, and model ensembling within the nnUNet v2 architecture. A single-modality baseline trained on the public ATLAS v2.0 dataset achieved competitive performance, and fine-tuning on an independent clinical cohort significantly improved cross-domain generalization. Incorporating FLAIR alongside T1-weighted MRI further improved segmentation accuracy, with the late-fusion ensemble achieving the best overall performance (Dice = 0.75; F1 = 0.80; ASD = 2.94 mm). Early fusion also yielded consistent gains when inter-modality alignment was precise, whereas mid-fusion offered limited benefit. Across all configurations, ensembling systematically improved voxel- and lesion-level accuracy, producing smoother and more spatially coherent boundaries—particularly for small and medium lesions. Nevertheless, lesion size remained a key challenge, with small lesions still more difficult to detect and delineate accurately.

These findings underscore the complementary value of multimodal MRI and demonstrate that fine-tuning and ensemble learning substantially improve lesion segmentation in heterogeneous, real-world clinical data. To further advance this work, we plan to curate and release a large, well-annotated dual-modality (T1-w + FLAIR) dataset for sub-acute and chronic stroke, addressing the current lack of publicly available multimodal resources. A larger, more diverse cohort will enable systematic analysis of factors contributing to segmentation failures, including lesion complexity, imaging quality, and scanner variability. Future efforts will also focus on refining lesion-aware loss functions, exploring uncertainty-aware fusion strategies, and promoting clinical translation through user-centered design and integration into neuroimaging workflows.

## CRediT authorship contribution statement

**Alessandro Di Matteo:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. **Youwan Mahé:** Writing – review & editing, Visualization, Software, Methodology. **Stéphanie Leplaideur:** Supervision, Investigation, Data curation. **Isabelle Boman:** Supervision, Investigation, Data curation. **Elise Bannier:** Supervision, Investigation, Data curation. **Francesca Galassi:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.patrec.2025.11.017.

## Data availability

The ATLAS v2.0 dataset is publicly available and can be accessed at https://fcon_1000 projects.nitrc.org/indi/retro/atlas.html. Data from internal studies are not publicly accessible but can be obtained from the corresponding author upon reasonable request.
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