# Comparing foundation models and nnU-Net for segmentation of primary brain lymphoma on clinical routine post-contrast T1-weighted MRI

Guanghui Fu, Lucia Nichelli, Dario Herran, Romain Valabregue, Agusti Alentorn, Khê Hoang-Xuan, Caroline Houillier, Didier Dormont, Stéphane Lehéricy, Olivier Colliot

<figure><img src="image_1.png" /></figure>

## To cite this version:

Guanghui Fu, Lucia Nichelli, Dario Herran, Romain Valabregue, Agusti Alentorn, et al. Comparing foundation models and nnU-Net for segmentation of primary brain lymphoma on clinical routine post-contrast T1-weighted MRI. SPIE Medical Imaging, SPIE, Feb 2025, San diego (Californie), United States. pp.45, 10.1117/12.3044679. [hal-04447318v5](https://hal.science/hal-04447318v5)

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Submitted on 27 May 2025

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# Comparing foundation models and nnU-Net for segmentation of primary brain lymphoma on clinical routine post-contrast T1-weighted MRI

Guanghui Fuª, Lucia Nichelliª,c,d, Dario Herranc, Romain Valabregueb,c, Agusti Alentornb,e, Khê Hoang-Xuanb,e, Caroline Houillierb,e, Didier Dormonta,d, Stéphane Lehéricyb,c,d, and Olivier Colliotª

ªSorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France

ᵇSorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France.

ᶜICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.

ᵈAP-HP, Pitié Salpêtrière, DMU DIAMENT, Dep. of Neuroradiology, Paris, France.

ᵉAP-HP, Pitié Salpêtrière, DMU Neurosciences, Dep. of Neurology 2, Paris, France.

## ABSTRACT

Primary Central Nervous System (PCNS) lymphoma is an aggressive brain tumor which segmentation on magnetic resonance imaging (MRI) is important to enhance diagnosis and follow-up. The rise of segmentation foundation models (FM) for segmentation offers a potential alternative to classical supervised deep learning approaches. There are generic computer vision FMs such as Segment Anything Model (SAM) and FMs specialized for medical images (e.g. MedSAM, UniverSeg). Nevertheless, their value on challenging tasks such as lymphoma segmentation on clinical routine data has not been thoroughly studied. In this paper, we assessed the performance of several FMs (SAM, MedSAM, UniverSeg) for lymphoma segmentation and compared them to a classical supervised learning with nnU-net. In addition, we performed experiments on the public dataset MSD-BraTS so that others can reproduce our findings. We found that nnU-net outperformed FMs by vast and statistically significant margins. For the lymphoma clinical routine dataset, the difference between nnU-Net and the best FM was about 19 percent points of Dice. For MSD-BraTS, it was about 10 percent points. The studied FMs fall short in these two complex segmentation tasks. The poor performance of MedSAM and UniverSeg was particularly disappointing as these FMs are dedicated to medical images. Thus, these approaches do not eliminate the need for task-specific models, at least in the case of brain tumour segmentation, and 3D supervised learning (e.g. with nnU-Net) remains an essential tool. Trained models for both tasks, code and box prompts (for MSD-BraTS) are available at: https://github.com/GuanghuiFU/medical_cv_foundation_eval.

**Keywords:** Lymphoma, Foundation model, SAM, MedSAM, UniverSeg, Brain tumour, Segmentation, Deep learning

## 1. INTRODUCTION

Primary central nervous system (CNS) lymphoma (PCNSL) is a rare cancer, characterized by its rapid development and high mortality rate, which poses significant challenges in clinical diagnosis, treatment planning and follow-up.¹ Automated segmentation tools are critical for clinical care as well as for research to deepen our understanding of the disease. However, few studies have been devoted to segmentation of PCNSL<sup>2,3</sup> unlike other types of brain tumours such as gliomas, e.g.<sup>4-6</sup> Brain tumour segmentation is a complex task due to variable location, shape and appearance. Besides, lymphomas often include a varying number of lesions, which is rare for gliomas. The task is even more complex when dealing with clinical routine data which is highly heterogeneous.

Further author information: (Send correspondence to Olivier Colliot, olivier.colliot@cnrs.fr)
Recently, foundation models (FM) for image segmentation have been proposed.<sup>7–10</sup> They have several potential advantages over classical fully-supervised segmentation models: they require none or limited annotated training data and reduce computational burden. Segment Anything Model (SAM)<sup>7</sup> is a generic computer vision FM which allows interactive segmentation with prompts. Specialized FMs for medical images have also been trained. UniverSeg<sup>11</sup> was trained on extensive medical imaging data. MedSAM<sup>12</sup> is a version of SAM fine-tuned on medical data. The authors claim that MedSAM can potentially eliminate the need for task-specific models.

Independent evaluations are needed to assess whether these FMs can replace fully-supervised segmentation. Several works have compared FMs to supervised approaches for medical image segmentation. However, most of them deal with SAM<sup>8–10,13–18</sup> and we could only find one evaluation paper on UniverSeg<sup>19</sup> and none on MedSAM. Three preprints<sup>20–22</sup> included an evaluation of MedSAM but these papers were focused on introducing a different method and not on benchmarking of FMs. One external evaluation of UniverSeg<sup>19</sup> found that its performance was comparable to that of nnU-Net.<sup>23</sup> However, it dealt with prostate segmentation, a task for UniverSeg was already pretrained and which is much simpler than lymphoma segmentation, due to the lower variability of the prostate in terms of shape, location and appearance. Moreover, many benchmarks<sup>8,9,13,14,17,18,20,24,25</sup> rely on prompts generated from ground truth which does not correspond to a “real-life” setting. Some studies<sup>14,15</sup> introduced randomness or jittering to ground-truth prompts but we don’t know if this correctly mimics the users habits.

Our study compares supervised learning, using nnU-net,<sup>23</sup> and FMs for segmentation of PCNS lymphoma on clinical routine MRI. We used a dataset of 112 patients with PCNSL acquired with different sequence parameters, sequence types (spin echo vs gradient echo), 2D or 3D sequences, resolutions, and MRI scanners. Since this clinical routine dataset cannot be shared, we complement our work with experiments on a subset of the open glioma dataset MSD-BraTS<sup>26,27</sup> in order for others to reproduce our work. Compared to previous evaluations, our study has the following novel features: i) comparison of MedSAM and UniverSeg in addition to SAM; ii) use of highly heterogeneous clinical routine data; iii) manual box prompts (rather than from ground-truth); iv) thorough evaluation with both inferential and descriptive statistics.

## 2. MATERIALS AND METHODS

### 2.1 Datasets

**Lymphoma dataset.** We studied 117 patients with primary CNS lymphoma. The study was approved by the Institutional Ethical Committee (Pitié Salpêtrière Hospital, Ile-de-France VI, n°DC-2009-957) and by the French Data Protection Authority (CNIL, Commission Nationale de l'Informatique et des Libertés, DR 2013-279). According to French regulation, consent was waived as these images were acquired as part of the routine clinical care of the patients. Each patient had a T1-weighted MRI after gadolinium injection. The images were acquired as part of clinical routine and were thus not harmonized (different MRI scanners, types of sequences, resolutions, contrast to noise, etc.). The full dataset was annotated by a certified neuroradiologist (L.N.) with four years of experience in neuro-oncology imaging. A subset of 50 cases was independently annotated by a certified radiologist (D.H.) with one year of experience in neuro-oncology imaging. The computed inter-rater variability (mean and 95% confidence interval computed using bootstrap on the 50 samples) metrics were: Dice coefficient 81.17 [77.76, 84.50]; Hausdorff 95th percentile distance: 6.99 [3.44, 11.46], topological 3D error: 2.12 [1.16, 3.38]. Please refer to section 2.3 for details about the metrics. The mean inter-rater Dice was greater than 80% indicating very good reliability. Each MRI volume in this dataset was resized and resampled to isotropic 1×1×1mm³ voxels and dimensions of 240×240×160 using resample and resize operations from TorchIO.<sup>28</sup> We also rescale the intensity to a range of 0 and 1. We removed five subjects because the preprocessing failed (wrong dimensions). The final lymphoma MRI dataset comprises 112 patients (60 females, 51 males, one unspecified) aged 32-86 years (mean 66.13). It includes 92 isotropic (or quasi-isotropic) 3D acquisitions and 20 non-isotropic 2D acquisitions across different field strengths (3T: 53 subjects, 1.5T: 53, 1.0T: 6), manufacturers (GE Healthcare: 93, Philips: 11, Siemens: 8). In-plane resolution ranges from 0.39 mm to 1.2 mm and slice thickness ranges from 0.29 mm to 8.0 mm. The example images in Figure 1 illustrate the variability in lesion appearance, location and image quality. The dataset was randomly split into a training/validation set with 57 patients and a test set with 55 patients. The test set was only used to validate the models.
<figure><img src="image_3.png" /><figcaption>Figure 1. Examples of clinical T1-weighted MR images of patients with PCNS lymphoma. Each example shows the MRI image, the same image overlaid with the ground truth segmentation, and a zoom in of the area(s) marked with a box. Please note that the boxes shown here correspond to the zoomed-in area and *not to the box* prompts provided to the *foundation models*.</figcaption></figure>

**MSD-BraTs dataset.** We also conducted experiments on the MSD-BRATS public dataset from the medical segmentation decathlon challenge.²⁶ BraTS is a dataset of patients with glioblastoma or lower-grade glioma, manually segmented by expert raters.²⁹ The downloaded dataset has already undergone uniform pre-processing, including co-registration to a common template, standardization to 1 mm³ resolution, and skull stripping. To be as similar as possible to lymphoma segmentation task, we selected the T1-weighted MRI after gadolinium injection and focused on the enhancing tumor as the target region. In order to have the approximately the same sample size for the two tasks, we randomly selected 113 patients for our experiments*. They were subsequently randomly split into a training/validation set with 58 patients and a test set comprising 55 patients. The test set was only used to evaluate the models.

## 2.2 Segmentation methods

**nnUNet.** nnUNet is a supervised deep-learning segmentation method²³ which has been shown to be one of the most consistently effective algorithms across many medical applications.²⁶ Initially, the pipeline autonomously extracts features from the dataset, including intensity distribution, image size, modalities, and more, to create data fingerprints. Subsequently, rule-based parameters utilize these dataset fingerprints to modify specific properties of the segmentation pipeline, following established heuristic rules. This includes the automatic selection of appropriate image resampling methods, batch sizes, etc. Moreover, certain robust configurations are predetermined, including the automatic determination of model architecture, training schedules, and implementation of data augmentation strategies. During the training time, 5-fold cross-validation is executed on the training/validation data for 1000 epochs of training, to select the optimal configuration.

**SAM and MedSAM.** SAM is an FM which has undergone training on a dataset comprising 11 million images and 1.1 billion masks.⁷ and demonstrating robust zero-shot performance across a diverse range of segmentation tasks.⁷ Its image encoder component is a Vision Transformer (ViT),³⁰ initially pre-trained as a Masked Autoencoder (MAE),³¹ and is minimally adapted to accommodate high-resolution inputs. The prompt encoder represents points of boxes prompt by utilizing positional encodings³² combined with learned embeddings for each type of prompt. The mask decoder part integrates the image and prompt embeddings with an output token to generate a mask, employing a modified Transformer decoder block³³ followed by a dynamic mask prediction head. Our experiments included various versions of the SAM model, including SAM-vit-b, SAM-vit-l, and SAM-vit-h. These models, trained with progressively larger numbers of masks, vary in their parameter sizes. MedSAM¹² is a medically fine-tuned version of the smallest SAM model variant (vit-b) which has been trained with over one million medical image-mask pairs. and was reported to consistently surpass the original SAM model in

<footnote>*Experiments on lymphoma include 112 patients because one preprocessing defect was only identified afterwards.</footnote>
performance. Both SAM and MedSAM require a prompt from the user for each image to be segmented. The prompt can take different forms including points and boxes. We chose box prompts which have been suggested to lead to better performance in medical segmentation tasks.¹⁵ One author (G.F.) manually annotated 3D box prompts in each test volume. The number of boxes is variable, to ensure complete coverage when multiple lesions are present. These 3D boxes are then transformed into 2D slice-level prompts. Utilizing these prompts, SAM (or MedSAM) infers the segmentation in each 2D slice. Subsequently, these predictions are stacked into 3D volume. The time taken to draw the box prompt for one image was 76.18±26.7 (in seconds; mean± standard-deviation) for lymphoma and 72.15±16.67 for MSD-BraTs. An example of 3D box prompt is depicted in Figure 2. In several other papers,⁸,⁹,¹²,¹³ box prompts were generated from ground truth segmentation (automatically computing a bounding box from the ground truth, possibly with perturbation). We believe this does not reflect realistic usage. However, as a comparison, we also provide results obtained by computing box prompts from ground truth in appendix A.

<figure><img src="image_4.png" /><figcaption>Figure 2. Example of a manually drawn 3D box prompt in a patient with lymphoma.</figcaption></figure>

**UniverSeg.** UniverSeg is designed to solve new medical segmentation tasks without retraining, leveraging the new CrossBlock mechanism for information transfer from the support set to the query image.¹¹ The support set is a small annotated dataset for the target task. This mechanism facilitates interaction between a query feature map and a set of support feature maps derived from both the query image and the support set. UniverSeg's training encompasses 53 datasets across 26 medical domains and 16 imaging modalities. The size of the support set can range from 1 to 64. They demonstrate that UniverSeg surpasses several models in diverse, held-out segmentation tasks, including those involving unseen anatomies, and even approaches the performance of fully supervised networks tailored for those specific tasks. In the UniverSeg experiments, we initially converted the 3D volumes into 2D slices, resizing them from their original dimensions of 240×240 to 128×128 to meet the model's architectural requirements. UniverSeg then processes each MRI slice in sequence. Upon completing all 2D slice predictions, these are resized to their original resolution (from 128×128 back to 240×240) and reconstituted into a 3D volume. The support set can include up to 64 images. We employed different strategies to examine the performance impact of different support sets. Moreover, UniverSeg generates soft predictions and we experimented with different thresholds. Results with different support sets and different thresholds are presented in Appendix B. In the rest of the paper, we present results obtained with the best performing support sets (mid for lymphoma, large for MSD-BraTs) and thresholds (0.8 for lymphoma and 0.7 for BraTS).

## 2.3 Performance metrics and statistical analysis

For selecting performance metrics, we followed the recommendations of Metrics Reloaded,³⁴ specifically employing the 3D Dice coefficient and the 95% 3D Hausdorff distance (HD95). We also computed the 3D connected component error (Topo 3D) which is the absolute difference in number of 3D connected components between the ground truth and prediction. For each metric, we report its mean value as well as the corresponding 95% confidence interval (CI) computed using bootstrap over the independent test set. For a subset of results (see Results and Discussion sections), we assessed whether the observed differences in Dice score between models were statistically significant using paired T-tests on the independent test set.
## 2.4 Code, data and trained models availability

We made the repository† publicly available, it contains: code, list of patients, box prompts, support sets for MSD-BraTS and trained models for both lymphoma and MSD-BraTS. MSD-BraTS data can be downloaded from the MSD website‡. We cannot provide data and prompts for the lymphoma dataset due to regulatory constraints.

# 3. RESULTS

## 3.1 Quantitative results

Results are presented in Table 1. Our experiments, conducted on both a private PCNS lymphoma and the public MSD-BraTS glioma dataset, yielded consistent findings. Supervised training with a 3D nnU-Net outperformed the best FM (SAM, vit-b version) by a vast margin: there was a statistically significant improvement of about 19 percent points of Dice ($p < 10^{-9}$) for the lymphoma dataset and of about 10 percent points for the MSD-BraTS ($p < 10^{-5}$). Similar results were observed for the HD95 metric: improvement of about 8m and 14mm with the 3D nnU-net compared to the best FM, for the lymphoma and the MSD-BraTS datasets respectively.

For the 3D topological errors, nnU-net also performed in general better compared to FMs, to the exception of MedSAM-vit-b which had similar results for this specific metric. Overall, findings from both tasks underscore that nnUNet-3D consistently outperformed FMs. Comparative examples of lymphoma segmentation by different models are illustrated in Figure 12.

<table><caption>Table 1. The performance of lymphoma and brain glioma segmentation on T1-weighted MRI from clinical dataset and MSD-BraTS dataset. Results presented as mean with 95% bootstrap confidence interval computed on the independent test set.</caption><thead><tr><th>Task</th><th>Model type</th><th>Model</th><th>Dice (in %)</th><th>HD95 (in mm)</th><th>Topo 3D</th></tr></thead><tbody><tr><td rowspan="5">Lymphoma</td><td rowspan="5">2D</td><td>UniverSeg</td><td>35.91 [29.33, 42.70]</td><td>73.89 [69.47, 78.41]</td><td>56.06 [51.22, 60.91]</td></tr><tr><td>SAM-vit-b</td><td>63.21 [57.96, 68.15]</td><td>21.98 [16.45, 28.11]</td><td>10.72 [5.54, 17.52]</td></tr><tr><td>SAM-vit-l</td><td>60.80 [55.66, 65.77]</td><td>21.12 [15.87, 27.02]</td><td>8.94 [4.93, 13.98]</td></tr><tr><td>SAM-vit-h</td><td>60.13 [54.98, 65.14]</td><td>22.46 [17.04, 28.40]</td><td>9.63 [5.65, 14.52]</td></tr><tr><td>MedSAM-vit-b</td><td>55.44 [49.78, 60.96]</td><td>22.83 [16.94, 29.35]</td><td>2.69 [1.80, 3.69]</td></tr><tr><td rowspan="2">3D</td><td>nnUNet-2d</td><td>73.99 [67.49, 80.06]</td><td>9.78 [6.03, 14.19]</td><td>1.41 [0.93, 1.94]</td></tr><tr><td>nnUNet-3d</td><td>82.08 [77.05, 86.44]</td><td>7.96 [4.63, 11.92]</td><td>1.19 [0.81, 1.59]</td></tr><tr><td rowspan="5">MSD-BraTS</td><td rowspan="5">2D</td><td>UniverSeg</td><td>51.42 [45.03, 57.89]</td><td>68.73 [63.81, 73.79]</td><td>98.33 [98.85, 104.51]</td></tr><tr><td>SAM-vit-b</td><td>69.96 [65.78, 73.80]</td><td>11.63 [9.28, 15.07]</td><td>17.58 [12.44, 23.56]</td></tr><tr><td>SAM-vit-l</td><td>69.77 [65.60, 73.60]</td><td>12.00 [9.63, 15.36]</td><td>14.62 [9.96, 19.95]</td></tr><tr><td>SAM-vit-h</td><td>67.76 [63.77, 71.43]</td><td>12.55 [10.23, 15.81]</td><td>14.80 [10.71, 19.58]</td></tr><tr><td>MedSAM-vit-b</td><td>46.11 [41.39, 50.78]</td><td>12.28 [9.95, 15.80]</td><td>8.31 [5.67, 11.69]</td></tr><tr><td rowspan="2">3D</td><td>nnUNet-2d</td><td>73.46 [66.76, 79.30]</td><td>11.90 [7.76, 17.19]</td><td>7.67 [5.07, 11.02]</td></tr><tr><td>nnUNet-3d</td><td>79.98 [74.42, 85.12]</td><td>6.47 [4.93, 8.31]</td><td>8.33 [5.61, 11.78]</td></tr></tbody></table>

Figures 3 and 4 display the Dice score distribution across the test set for different models for lymphoma and MSD-BraTS tasks. For the lymphoma dataset, the variability (represented by the interquartile range - IQR) is lower for the best performing model (3D nnU-net) compared to other models. For the BraTS dataset, the IQR is similar for nnU-Net-3D and SAM, despite a much higher median value for the former. MedSAM, UniverSeg and nnU-Net-2D display a larger variability. In general, the variability tends to be higher for lower performing models. Nevertheless, note that for all models there are a few patients for which the performance is very poor.

## 3.2 Influence of the rater on lymphoma segmentation results

We performed an additional evaluation on 30 lymphoma patients annotated by the second radiologist (D.H.) which were part of the test set (unfortunately, 20 of them are from training set patients and thus unusable for testing). As described in Section 2.1, D.H. contributed 50 annotations for the purpose of calculating inter-rater

<footnote>†https://github.com/GuanghuiFU/medical_cv_foundation_eval</footnote>

<footnote>‡http://medicaldecathlon.com/</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 3. Boxplot illustrating the distribution of Dice score in different models for lymphoma data. Every box is bounded by the lower quartile (Q1) and the upper quartile (Q3), with the centre line representing the median. Whiskers extend from the box to the furthest data point within 1.5x the interquartile range of the box.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 4. Boxplot illustrating the distribution of Dice score in different models for MSD-BraTs data. Every box is bounded by the lower quartile (Q1) and the upper quartile (Q3), with the centre line representing the median. Whiskers extend from the box to the furthest data point within 1.5x the interquartile range of the box.</figcaption></figure>
reliability, 30 of which were part of the test set. We evaluated the performance on these 30 annotations to study how models perform with annotations provided by another radiologist. In particular, we were interested to study if nnU-Net took advantage of using annotations from the same rater (as is the case for the results presented in Table 1 which rely on annotations from L.N.). Results are presented in Table 2. There is a small decrease in performance for nnU-Net-3D (~3 percent points) but it remains vastly superior to FMs. The difference for nnU-Net-2D is larger (~6 points) but its performance remains above that of all FMs and vastly superior to MedSAM and UniverSeg. To conclude, it seems that there is little influence of the choice of the rater on the performance of nnU-Net-3D.

<table><caption>Table 2. The performance of lymphoma segmentation on labels provided by the second annotator (D.H.) on clinical dataset T1-weighted MRI. Results presented as mean with 95% bootstrap confidence interval computed on the independent test set.</caption><thead><tr><th>Task</th><th>Model type</th><th>Model</th><th>Dice (in %)</th><th>HD95 (in mm)</th><th>Topo 3D</th></tr></thead><tbody><tr><td rowspan="6">Lymphoma</td><td rowspan="5">2D</td><td>UniverSeg</td><td>29.16 [20.50, 37.99]</td><td>64.5 [57.79, 71.41]</td><td>30.03 [25.23, 35.13]</td></tr><tr><td>SAM-vit-b</td><td>62.56 [56.30, 68.23]</td><td>29.50 [21.52, 38.22]</td><td>12.37 [5.63, 23.4]</td></tr><tr><td>SAM-vit-l</td><td>60.74 [54.5, 66.47]</td><td>27.91, [20.35, 36.52]</td><td>9.97 [5.0, 17.87]</td></tr><tr><td>SAM-vit-h</td><td>60.33 [54.04, 66.07]</td><td>28.31 [20.75, 36.91]</td><td>11.83 [5.83, 20.1]</td></tr><tr><td>MedSAM-vit-b</td><td>53.16 [45.70, 60.27]</td><td>29.87 [21.44, 38.84]</td><td>4.87 [2.73, 7.6]</td></tr><tr><td>nnUNet-2d</td><td>67.66 [58.10, 76.36]</td><td>16.57 [9.41, 24.49]</td><td>2.83 [1.53, 4.8]</td></tr><tr><td>3D</td><td>nnUNet-3d</td><td>77.02 (69.82, 82.83)</td><td>14.44 [7.38, 22.49]</td><td>3.07 [1.7, 4.97]</td></tr></tbody></table>

## 3.3 Visualizations of lymphoma segmentation results

Figures 5, 6 and 7 display 2D results of the different models for the three patients with lymphoma shown in 3D in Figure 12. For each case, we comment upon the results of each model in the caption.

## 3.4 Visualizations of MSD-BraTS segmentation results

Figure 8 displays the 3D renderings of results obtained for three representative patients from MSD-BraTS. Figures 9, 10 and 11 display 2D results of the different models for these three patients.

# 4. DISCUSSION

In this paper, we compared the segmentation performance of supervised learning with nnU-Net and various FMs on two datasets: a local clinical routine lymphoma dataset and the public MSD-BraTS glioma dataset. Results were consistent across datasets and demonstrated that 3D nnU-Net outperformed FMs by a vast margin.

Even though the performance gap was already large (10 percent points of Dice) and statistically significant for the MSD-BraTS dataset, it was huge for the lymphoma dataset (19 percent points). This difference comes mainly from a lower performance of FMs while that of nnU-Net was quite stable. There may be different explanations to this. The lymphoma dataset has a wide heterogeneity which reflects clinical routine. Multiple distant brain lesions is commonly seen in patients with lymphoma, while it is rare in patients with gliomas. The lower performance of SAM could in principle be expected as it was not trained medical data and this limitation is acknowledged by the authors.⁷¹³,¹⁴,¹⁶ also found that SAM was outperformed by U-nets by vast margins over different medical tasks. However, the massive superiority of nnU-net is at odds with the results reported in the original papers of MedSAM¹² and UniverSeg.¹¹ The MedSAM paper reports excellent Dice scores which are comparable to that of a U-Net.¹¹ UniverSeg was reported to have lower performance than nnU-net¹¹ but the difference was much lower than in our experiments (13 Dice points in original paper versus 46 points for lymphoma and 28 points for MSD-BraTS). Among FMs, SAM-based models gave in general better performance. The smaller SAM-vit-b model tended to work slightly better than the larger version, but confidence intervals are overlapping. Surprisingly, MedSAM performed worse than the corresponding non-medical SAM-bit-b, by vast and statistically significant margins (8 points of Dice for lymphoma, *p* < 10⁻⁶; 23 points for MSD-BraTS, *p* < 10⁻¹³). This is in contradiction with what was reported in the original paper¹² where superior performance compared to SAM was observed. UniverSeg resulted in very poor performances, as it was not only vastly outperformed by nnU-Net but also by SAM (28 Dice points for lymphoma, *p* < 10⁻⁸; 18 points for MSD-BraTS,
<figure><img src="image_7.png" /><figcaption>Figure 5. Ground-truth label vs predictions on lymphoma data: case (a). Upper row: full MRI. Lower row: zoom-in. FMs results are all very poor. They are all inconsistent across slices. UniverSeg vastly underestimates the lesion boundaries but also produces many false positives. In these cases, it is better appreciated in the 3D renderings in Figure 12. SAM and MedSAM both provide massive over- and under-estimations depending on the area. nnU-Net-2D completely misses some slices as was already clear from Figure 12. The result of nnU-Net-3D is in general acceptable even though it misses small parts of the tumor. Note that we can also see that the ground-truth label has some minor imperfections.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 6. Ground-truth label vs predictions on lymphoma data: case (b). Upper row: full MRI. Lower row: zoom-in. FMs results are overall poor. UniverSeg misses a large part of the bigger lesion and, again, produces many false positives (see also Figure 12). SAM and MedSAM provide very poor results on many slices. nnU-Net-2D misses one lesion. The results of nnU-Net-3D are good, in particular it is able to segment all lesions. Note that we can also see that the radiologist took a conservative margin in that specific annotation (the overall contour is about one voxel smaller than in the other cases).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 7. Ground-truth label vs predictions on lymphoma data: case (c). Upper row: full MRI. Lower row: zoom-in. FMs display various errors even though less than in Figures 5 and 6. UniverSeg tends to underestimate the lesion boundaries and, as in almost every case, produces false positives remote from the lesion (see also Figure 12). SAM and MedSAM in general overestimate lesion boundaries and are inconsistent across slices due to their 2D nature. nnU-Net-2D and nnU-Net-3D both look satisfactory even though nnU-Net-3D is more accurate.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 8. Ground-truth label vs predictions on three representative patients from MSD-BraTS. Left: ground-truth superimposed on MRI. Right: 3D renderings of ground-truth (label) and of predictions with different methods. As for lymphomas, FMs do not recover the 3D shape of the tumor. Besides, UniverSeg produces a lot of false positives distant from the lesion.</figcaption></figure>

$p < 10^{-7}$). Lower performance on MSD-BraTS is particularly surprising as UniverSeg was trained on BraTS (even though not the same task and with more modalities than in our experiments). The results we report are the best that we obtained across different thresholds and support sets (see Appendix B). Qualitatively, we could notice that UniverSeg tended to focus only on segments with similar contrast, neglecting the anatomical location inside the data. This limitation becomes particularly evident given the variable position and contrast of lymphoma.³⁵

The 3D nnUNet demonstrated superior performance over its 2D version. It is thus possible that the poor performance of FMs is in part due to their 2D nature which prevents them from using the complex 3D information. Nevertheless, the performance of FMs was still inferior to that of the 2D nnUNet, indicating that this is likely not the only limitation.

Some previous works have generated prompts from ground truth. For comparison, we also provided results obtained when generating prompts from ground truth (Appendix A). The only setting that led to comparable results between SAM and nnU-Net was when 2D prompts generated from ground truth were provided. We believe this is clearly not a realistic scenario as the box perfectly fits the ground truth on every slice.

Our work shows that the studied FMs, even UniverSeg and MedSAM which are dedicated to medical images, are unsuitable for two important segmentation tasks. There may be several underlying explanations. As mentioned above, the 2D nature of the studied FMs is certainly a drawback but is likely not the only explanation given their overall poor delineation of contours. Regarding UniverSeg, it seems that the support set mechanism is insufficient to adapt to complex lesions disseminated throughout the brain. Regarding MedSAM, it appears that its fine-tuning on medical images is not enough to make it a universal tool. These observations underscore the importance of clearly defining the usage scenarios for UniverSeg and MedSAM.

Our work has the following limitations. First, our results only concern two segmentation tasks, which are particularly difficult, and the situation could be different for other tasks. Moreover, the field of FMs is advancing fastly. It is very well possible that better results could be obtained with newer FMs. Furthermore, our provided
<figure><img src="image_11.png" /><figcaption>Figure 9. Ground-truth label vs predictions on MSD-BraTS data: case (a). Upper row: full brain. Lower row: zoom-in. All FMs perform poorly but MedSAM and UniverSeg results are particularly disastrous. UniverSeg massively undersegments, while also producing false positive remote from the main lesion. MedSAM massively oversegments on one side of the tumor and, at the same time, largely misses the other side. SAM generates several false positive areas, as can be better seen on coronal and sagittal slices. nnU-Net-2D makes several errors but still does much better than FMs. nnU-Net-3D is overall the best performing even though some defects can be seen on the axial slice.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Figure 10. Ground-truth label vs predictions on MSD-BraTs data: case (b). Upper row: full brain. Lower row: zoom-in. All FMs result in a poor segmentation. UniverSeg mostly undersegment the tumor. SAM and MedSAM struggle finding the contours, alternating between over- and under-segmentations. nnU-Net-2D and nnU-Net-3D both do a decent job even though the 3D version misses a part of the tumor which is partly recovered by the 2D model. The latter phenomenon was rarely found (in general, the nnU-Net-3D provides better results).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure 11. Ground-truth label vs predictions on MSD-BraTs data: case (c). Upper row: full brain. Lower row: zoom-in. Both FMs and nnU-Net-2D do a poor job, even though the results of FMs are substantially worse. UniverSeg misses most of the tumor. SAM and MedSAM have a very hard time identifying boundaries. As for nnU-Net-2D, even though the axial plane looks decent, it misses important parts of the tumor. nnU-Net-3D is clearly better than all other models. However, one can appreciate (mostly on the sagittal slice) that the boundaries are not particularly accurate.</figcaption></figure>
prompts may have imperfections. It is also possible that we have used UniverSeg in a suboptimal way, even though we experimented with different support sets and thresholds. We have made prompts and support sets openly available for MSD-BraTS for other researchers to experiment with them and would be happy to be shown that there was a more optimal way to use FMs.

<figure><img src="image_14.png" /><figcaption>Figure 12. Ground-truth label vs predictions on three representative patients with lymphoma. Left: ground-truth superimposed on MRI. Right: 3D renderings of ground-truth (label) and of predictions with different methods. One can see that FMs generally provide very poor results. In particular, we can observe that they are not able to accurately find the contours and cannot recover the 3D shape. UniverSeg gives massive false positives remote from the lesions. nnU-Net-2D can, in some cases, miss slices. nnU-Net-3D are overall satisfactory. More examples, including segmentation results on 2D slices for all results will be presented in the proceedings version but could not be included in the abstract due to the 4-page limit.</figcaption></figure>

## 5. ACKNOWLEDGMENTS

The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program (reference ANR-19-P3IA-0001, project PRAIRIE 3IA Institute and reference ANR-10-IAIHU-06, project Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), from Agence Nationale de la Recherche (reference ANR-23-CE17-0054, project LYMP-PD), from the European Union's Horizon Europe Framework Programme (grant number 101136607, project CLARA), from Inserm and the French Ministry of Health in the context of the MESSIDORE 2023 call operated by IReSP (reference AAP-2023-MSDR-341011), and by the Fonds Recherche Neurosciences under the project "NEIMO - Monitoring Neuroinflammation: a Paris Brain Institute-Yale Neurology Collaborative program". Guanghui Fu is supported by the Chinese Government Scholarship provided by China Scholarship Council (CSC). Lucia Nichelli is supported by an Inria/AP-HP "Poste d'accueil".

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APPENDIX A. GENERATING BOX PROMPTS FROM GROUND TRUTH SEGMENTATION

In this study, we manually constructed prompts for SAM and MedSAM experiments. Some other works have generated prompts from ground truth. We do not advocate for such approach as we believe it is a circular reasoning: if real labels are already available, automated methods would be redundant. Nevertheless, we conducted experiments to compare the performance when generating prompts from ground-truth to our manual box prompts. The details of these experiments are as follows:

* 3D-manual: our manually labeled 3D box prompts.
* 3D-gt-single: a single 3D prompt box generated from the ground-truth defined as the smallest 3D area which encompasses all lesions.
* 3D-gt-multi: multiple 3D boxes are generated: one for each connected component of the ground-truth (only relevant for lymphomas).
* 2D-gt-single: a different 2D box prompt for each slice in the axial plane. Each box is the smallest 2D area encompassing all lesions in a given slice.
* 2D-gt-multi: same as 2D-gt-single but with possibly multiple prompts per slice, corresponding to the different lesions (only relevant for lymphomas).

Based on the results presented in Table 1, the SAM-vit-b model achieved the best performance among SAM models. Consequently, we selected this version for our experiments. Results with different box prompts are provided in Table S1.

The experimental results indicate that SAM-b with manual annotation (3D-manual) outperforms a single 3D box prompt box generated from real labels (3D-gt-single). The advantage of 3D-manual over 3D-gt-single is attributed to our manual annotations focusing on the main lesion area, whereas 3D-gt-single may include unnecessarily large borders due to pixels distant from the lesion center, adversely affecting performance. This effect is particularly noticeable in lymphoma data. On the contrary, the 3D-gt-multi approach, where a bounding box is drawn for each connected region, resulted in a substantial improvement but the Dice was still about 9 percent points lower than that of the 3D nnU-Net. Nevertheless, it resulted in a small improvement in HD95. The generation of 2D box prompts from ground truth resulted in massive performance enhancement providing results which are better than those of nnU-Net (the difference is moderate in terms of Dice score but substantial in terms of HD95). This demonstrates that SAM-based segmentation models can well capture lesion borders when given optimal prompts. However, this correspond to a completely unrealistic scenario as the ground-truth was used to generate 2D boxes that perfectly match the lesions on every slice.

APPENDIX B. UNIVERSEG UNDER DIFFERENT THRESHOLDS

As mentioned above, we test different strategies to build the support set. Specifically, we chose one slice from each training scan, selecting those with the largest, smallest, and medium lesion proportions (denoted as large, small, and mid). We conducted an additional experiment where we combined slices with the largest and smallest lesion proportions to investigate performance at the maximum setting of 64 images (large+small experiment). Also, we experimented with different thresholdings of the soft predictions generated by UniverSeg, from 0.1 to 0.9. Results are presented in Table S2 and Figures S1 and S2. The small support set setting produces almost no predictions. For lymphoma, large and mid setting provided comparable results. For MSD-BraTS, the large setting proved more efficient. Predictions with thresholds below 0.7 tended to segment all high-signal regions, resulting in many meaningless regions.
<table><caption>Table S1. Comparison between our 3D manual box prompts and different box prompts generated from ground-truth (denoted as gt). Please refer to the text for the explanation of the different strategies used to generate prompts from ground truth. Results presented as mean with 95% bootstrap confidence interval computed on the independent test set.</caption><thead><tr><th>Data</th><th>Prompt</th><th>Model</th><th>Dice</th><th>HD95</th><th>Topo3D</th></tr></thead><tbody><tr><td rowspan="7">Lymphoma</td><td>3D-manual</td><td rowspan="7">SAM-vit-b</td><td>63.21 [57.96, 68.15]</td><td>21.98 [16.45, 28.11]</td><td>10.72 [5.54, 17.52]</td></tr><tr><td>3D-gt-single</td><td>45.82 [37.84, 53.80]</td><td>19.61 [15.89, 23.60]</td><td>58.88 [38.80, 82.54]</td></tr><tr><td>3D-gt-multi</td><td>73.12 [70.03, 76.28]</td><td>6.17 [5.27, 7.12]</td><td>10.19 [6.24, 14.74]</td></tr><tr><td>2D-gt-single</td><td>75.17 [69.73, 80.16]</td><td>7.50 [5.32, 9.90]</td><td>9.09 [4.57, 14.22]</td></tr><tr><td>2D-gt-multi</td><td>86.33 [84.35, 88.24]</td><td>1.91 [1.68, 2.19]</td><td>3.96 [2.20, 5.91]</td></tr><tr><td>None</td><td>73.99 [67.49, 80.06]</td><td>9.78 [6.03, 14.19]</td><td>1.41 [0.93, 1.94]</td></tr><tr><td>None</td><td>82.08 [77.05, 86.44]</td><td>7.96 [4.63, 11.92]</td><td>1.19 [0.81, 1.59]</td></tr><tr><td rowspan="7">MSD-BraTs</td><td>3D-manual</td><td rowspan="7">SAM-vit-b</td><td>69.96 [65.78, 73.80]</td><td>11.63 [9.28, 15.07]</td><td>17.58 [12.44, 23.56]</td></tr><tr><td>3D-gt-single</td><td>64.68 [60.55,68.95]</td><td>14.23 [12.46, 16.46]</td><td>25.62 [18.89, 33.78]</td></tr><tr><td>2D-gt-single</td><td>82.22 [79.39, 84.90]</td><td>4.46 [3.78, 5.23]</td><td>7.45 [5.04, 10.35]</td></tr><tr><td>None</td><td>73.46 [66.76, 79.30]</td><td>11.9 [7.76, 17.19]</td><td>7.67 [5.07, 11.02]</td></tr><tr><td>None</td><td>79.98 [74.42, 85.12]</td><td>6.47 [4.93, 8.31]</td><td>8.33 [5.61, 11.78]</td></tr></tbody></table>

<figure><img src="image_15.png" /><figcaption>Figure S1. The Dice score of UniverSeg across varying thresholds for the lymphoma dataset.</figcaption></figure>
<table><caption>Table S2. Influence of different strategies to define the support set and of different threshold on UniverSeg results. Results presented as mean with 95% bootstrap confidence interval computed on the independent test set.</caption><thead><tr><th>Data</th><th>Support set</th><th>Threshold</th><th>Dice</th><th>HD95</th><th>Topo 3D</th></tr></thead><tbody><tr><td rowspan="20">Lymphoma</td><td rowspan="8">large (57)</td><td>0.1</td><td>7.65 [5.68, 9.89]</td><td>92.82 [88.81, 96.89]</td><td>168.81 [154.48, 182.70]</td></tr><tr><td>0.2</td><td>12.15 [9.15, 15.51]</td><td>89.54 [85.32, 93.75]</td><td>170.02 [158.37, 181.76]</td></tr><tr><td>0.3</td><td>16.41 [12.58, 20.67]</td><td>87.55 [83.31, 91.87]</td><td>165.20 [153.91, 176.44]</td></tr><tr><td>0.4</td><td>20.59 [16.04, 25.65]</td><td>85.67 [81.41, 90.02]</td><td>158.72 [148.74, 168.59]</td></tr><tr><td>0.5</td><td>24.38 [19.17, 30.07]</td><td>84.12 [79.81, 88.56]</td><td>136.41 [128.74, 143.98]</td></tr><tr><td>0.6</td><td>27.79 [22.01, 33.99]</td><td>82.60 [78.08, 87.22]</td><td>115.89 [109.20, 122.65]</td></tr><tr><td>0.7</td><td>30.93 [24.77, 37.41]</td><td>80.46 [75.77, 85.25]</td><td>91.35 [85.39, 97.33]</td></tr><tr><td>0.8</td><td>33.65 [27.15, 40.45]</td><td>78.01 [73.32, 82.91]</td><td>68.76 [63.94, 73.54]</td></tr><tr><td>0.9</td><td>35.43 [28.59, 42.21]</td><td>73.96 [69.13, 78.81]</td><td>41.33 [37.96, 44.87]</td></tr><tr><td rowspan="8">mid (57)</td><td>0.1</td><td>9.71 [7.43, 12.34]</td><td>91.41 [87.30, 95.47]</td><td>223.13 [206.89, 239.02]</td></tr><tr><td>0.2</td><td>15.80 [12.31, 19.69]</td><td>86.60 [82.44, 90.83]</td><td>202.15 [189.06, 214.89]</td></tr><tr><td>0.3</td><td>20.93 [16.49, 25.77]</td><td>84.05 [79.90, 88.36]</td><td>176.35 [164.52, 187.57]</td></tr><tr><td>0.4</td><td>25.40 [20.29, 30.98]</td><td>82.12 [77.89, 86.51]</td><td>152.70 [142.44, 162.69]</td></tr><tr><td>0.5</td><td>29.12 [23.58, 35.08]</td><td>80.22 [75.99, 84.57]</td><td>131.52 [122.41, 140.28]</td></tr><tr><td>0.6</td><td>32.09 [26.18, 38.33]</td><td>78.38 [74.16, 82.70]</td><td>104.54 [97.15, 111.57]</td></tr><tr><td>0.7</td><td>34.47 [28.22, 41.00]</td><td>76.53 [72.18, 80.93]</td><td>79.13 [73.24, 85.09]</td></tr><tr><td>0.8</td><td>35.91 [29.33, 42.70]</td><td>73.89 [69.47, 78.41]</td><td>56.06 [51.22, 60.91]</td></tr><tr><td rowspan="8">large+small (64)</td><td>0.9</td><td>35.50 [28.73, 42.37]</td><td>67.51 [62.50, 72.63]</td><td>31.07 [27.87, 34.41]</td></tr><tr><td>0.1</td><td>10.00 [7.60, 12.76]</td><td>91.73 [87.66, 95.73]</td><td>239.76 [224.17, 254.91]</td></tr><tr><td>0.2</td><td>16.13 [12.46, 20.23]</td><td>88.53 [84.31, 92.70]</td><td>224.91 [210.74, 238.96]</td></tr><tr><td>0.3</td><td>21.05 [16.55, 26.05]</td><td>86.26 [81.93, 90.61]</td><td>197.76 [184.67, 210.31]</td></tr><tr><td>0.4</td><td>25.02 [19.85, 30.61]</td><td>84.02 [79.70, 88.47]</td><td>169.83 [158.31, 181.57]</td></tr><tr><td>0.5</td><td>27.94 [22.25, 33.83]</td><td>81.82 [77.27, 86.44]</td><td>141.13 [130.81, 150.98]</td></tr><tr><td>0.6</td><td>29.85 [23.89, 36.04]</td><td>80.09 [75.37, 84.81]</td><td>111.61 [103.20, 119.65]</td></tr><tr><td>0.7</td><td>30.99 [24.81, 37.34]</td><td>78.17 [73.38, 82.90]</td><td>83.39 [76.89, 89.70]</td></tr><tr><td rowspan="8">MSD-BraTs</td><td rowspan="8">large (57)</td><td>0.8</td><td>31.09 [24.71, 37.69]</td><td>75.81 [70.96, 80.62]</td><td>57.56 [52.69, 62.33]</td></tr><tr><td>0.9</td><td>29.54 [22.97, 36.24]</td><td>70.49 [65.48, 75.59]</td><td>32.50 [29.39, 35.72]</td></tr><tr><td>0.1</td><td>14.85 [12.56, 17.30]</td><td>83.10 [80.14, 86.26]</td><td>120.85 [113.65, 128.31]</td></tr><tr><td>0.2</td><td>22.02 [18.84, 25.34]</td><td>81.30 [78.27, 84.50]</td><td>121.87 [113.80, 129.69]</td></tr><tr><td>0.3</td><td>29.46 [25.55, 33.57]</td><td>80.68 [77.59, 83.94]</td><td>139.18 [129.78, 148.73]</td></tr><tr><td>0.4</td><td>37.50 [32.94, 42.24]</td><td>79.99 [76.72, 83.39]</td><td>153.24 [143.96, 162.05]</td></tr><tr><td>0.5</td><td>44.65 [39.34, 50.04]</td><td>78.44 [74.63, 82.05]</td><td>154.25 [146.71, 161.65]</td></tr><tr><td>0.6</td><td>49.64 [43.83, 55.53]</td><td>74.96 [70.84, 79.23]</td><td>141.45 [133.64, 148.80]</td></tr><tr><td rowspan="8">mid (57)</td><td>0.7</td><td>51.42 [45.03, 57.89]</td><td>68.73 [63.81, 73.79]</td><td>98.33 [91.85, 104.51]</td></tr><tr><td>0.8</td><td>49.16 [42.32, 56.08]</td><td>53.89 [47.27, 60.63]</td><td>53.24 [48.84, 57.62]</td></tr><tr><td>0.9</td><td>41.43 [34.60, 48.43]</td><td>30.75 [24.14, 37.87]</td><td>18.76 [15.84, 21.91]</td></tr><tr><td>0.1</td><td>11.14 [8.94, 13.45]</td><td>87.80 [84.56, 91.10]</td><td>208.62 [197.42, 220.80]</td></tr><tr><td>0.2</td><td>18.06 [14.42, 21.83]</td><td>82.11 [77.84, 86.44]</td><td>215.65 [201.93, 229.13]</td></tr><tr><td>0.3</td><td>18.66 [14.44, 23.15]</td><td>64.27 [59.86, 68.72]</td><td>123.73 [113.20, 134.11]</td></tr><tr><td>0.4</td><td>12.86 [9.19, 16.88]</td><td>52.37 [46.91, 57.77]</td><td>63.98 [56.91, 70.96]</td></tr><tr><td>0.5</td><td>7.02 [4.47, 9.94]</td><td>39.89 [35.11, 44.74]</td><td>29.38 [24.95, 33.67]</td></tr><tr><td>0.6</td><td>3.24 [1.83, 4.96]</td><td>34.44 [30.73, 38.43]</td><td>13.96 [11.13, 17.05]</td></tr><tr><td rowspan="4">mid (57)</td><td>0.7</td><td>1.20 [0.59, 1.92]</td><td>∞</td><td>8.60 [6.20, 11.76]</td></tr><tr><td>0.8</td><td>0.38 [0.11, 0.75]</td><td>∞</td><td>8.55 [5.96, 11.71]</td></tr><tr><td>0.9</td><td>0.11 [0.01, 0.27]</td><td>∞</td><td>9.22 [6.42, 12.60]</td></tr></tbody></table>
<figure><img src="image_16.png" /><figcaption>Figure S2. The Dice score of UniverSeg across varying thresholds for the MSD-BraTS dataset.</figcaption></figure>