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REVIEW 4 major objections 6 minor 32 references

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings

T0 review · 4 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A three-network ensemble with STAPLE fusion beats each individual model for glioma segmentation on Sub-Saharan African MRI.

desk verdict A plausible ensemble benchmark on BraTS-SSA undermined by an internal contradiction between Tables 2 and 3 that must be resolved before the headline claim can be trusted. read the letter →

arxiv 2502.02179 v1 pith:GNH3CXXI submitted 2025-02-04 eess.IV cs.CV

Jeremiah Fadugba , Isabel Lieberman , Olabode Ajayi , Mansour Osman , Solomon Oluwole Akinola , Tinashe Mustvangwa , Dong Zhang , Udunna C Anazondo
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Raymond Confidence
This is my paper · ORCID
classification eess.IVcs.CV
keywords deeplearningimagesegmentationbraintumorgliomaensembleSTAPLEBraTS-Africaresource-limitedsettings
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to show that a deep ensemble of three 3D convolutional networks—UNet3D, V-Net, and MSA-VNet—combined by the STAPLE fusion algorithm produces more accurate glioma segmentation on Sub-Saharan African MRI than any of the three models on its own, in a setting with only 60 training scans and a shortage of expert radiologists. The authors pretrain each model on a general glioma dataset, fine-tune on the African dataset, freeze the decoder of the largest model to limit overfitting, and prune small enhancing-tumor components as post-processing. On the challenge validation set, the ensemble reaches DICE scores of 0.8167 for enhancing tumor, 0.8358 for tumor core, and 0.8521 for whole tumor, outperforming every individual model. If this result holds on unseen data, the pipeline offers a practical, relatively low-cost blueprint for automated tumor delineation in resource-limited hospitals.

What carries the argument

The load-bearing component is STAPLE (Simultaneous Truth and Performance Level Estimation), a probabilistic label-fusion algorithm that jointly estimates the hidden true segmentation and each model's performance level, then merges the three predicted volumes into one consensus segmentation. It is supported by a two-stage training schedule—pretraining on the general glioma set and fine-tuning on the African set, with the decoder of the largest model frozen—and by a post-processing step that deletes enhancing-tumor connected components of 50 voxels or fewer and fills any holes this creates in the tumor-core region. The mechanism converts disagreeing model votes into a single, more stable volume.

What would settle it

Run the identical frozen pipeline on the official BraTS-SSA test set or on an independently collected African glioma cohort and compare the STAPLE ensemble against the best single model, V-Net. If the ensemble does not beat V-Net on that held-out set, or if its DICE falls materially below 0.8358 (tumor core), 0.8521 (whole tumor), and 0.8167 (enhancing tumor), the central claim is refuted; an audit of whether validation labels entered model or threshold selection would settle the measurement question.

Watch

Extended reading notes

Core claim

The central claim is that fusing diverse 3D segmentation networks raises accuracy on a Sub-Saharan African glioma dataset beyond what any single architecture achieves. The paper reports that STAPLE fusion of UNet3D, V-Net, and MSA-VNet, after transfer from the general glioma dataset to the African dataset, yields DICE scores of 0.8358 for tumor core, 0.8521 for whole tumor, and 0.8167 for enhancing tumor on the BraTS-SSA validation set. Relative to the UNet3D baseline, this is a 6.4%, 8.6%, and 3.9% improvement for ET, TC, and WT, respectively. The paper also finds that V-Net is the strongest single model and that MSA-VNet's attention mechanism does not improve over the baseline, so the ensemble gain is attributed to model diversity and label fusion rather than to any one architecture.

Load-bearing premise

The whole result rests on treating the online validation set as a fixed held-out set; if the ensemble, STAPLE, or the 50-voxel threshold was tuned against those labels, the reported DICE numbers are optimistic.

Editorial extensions

If this is right

  • If the validation results generalize, the same pipeline becomes a strong published baseline for the BraTS-Africa segmentation challenge.
  • When only a single model can be deployed, the modified V-Net with 5x5x5 volumetric kernels is the better choice on this data distribution.
  • A 60-scan African cohort is enough to adapt models pretrained on other populations, which matters where local annotated MRI is scarce.
  • The small but consistent gain from removing tiny enhancing-tumor components shows that simple post-processing should be retained in clinical deployment.
  • Training with four moderately powered GPUs keeps the approach within reach of centers without large compute clusters.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not test whether the validation set influenced the choice of ensemble, STAPLE, or the 50-voxel post-processing threshold; if it did, the reported margins over single models will likely shrink on a truly unseen test set.
  • The finding that freezing the decoder helped only the largest model suggests a general low-data recipe: constrain capacity in proportion to model size during domain transfer.
  • MSA-VNet's underperformance may be specific to this dataset's image quality and lesion variability, so this single result should not be read as evidence against multi-scale attention mechanisms in brain tumor segmentation.
  • STAPLE's per-model performance estimates could be reused to output voxel-level confidence maps, giving radiologists a principled way to spotlight regions where the automated contour is uncertain.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a deep ensemble for brain tumor segmentation combining UNet3D, V-Net, and MSA-VNet, with STAPLE fusion, trained on BraTS-GLI and fine-tuned on BraTS-SSA, targeting Sub-Saharan African populations. Results are reported on a held-out BraTS-SSA test set (Table 1), on the Synapse validation leaderboard (Table 2), and via 5-fold cross-validation on the validation set (Table 3). The headline claim is that the ensemble significantly outperforms individual models, with DICE scores of 0.8167 (ET), 0.8358 (TC), and 0.8521 (WT).

Significance. If the results are valid, the work addresses a clinically important and under-served setting (glioma segmentation in Sub-Saharan Africa) and demonstrates that a relatively simple ensemble of established architectures plus STAPLE fusion can reach competitive DICE scores on the BraTS-SSA benchmark. The use of the official Synapse validation leaderboard as an external evaluation is a strength, as is the practical focus on resource-constrained deployment. However, the paper's central claim of significant ensemble superiority is not currently supported by the evidence as presented, and the internal inconsistency between Tables 2 and 3 needs to be resolved before the contribution can be assessed.

major comments (4)
  1. [Section 4.3, Tables 2 and 3] The central claim that the ensemble 'significantly outperforms individual models' is internally contradicted by the paper's own Table 3. Table 3 reports V-Net mean DICE of 0.8365 (ET), 0.8567 (TC), and 0.8490 (WT) on the 'BraTS 2024 SSA Validation set,' while Table 2 reports the ensemble's scores on the same named set as 0.8167 (ET), 0.8358 (TC), and 0.8521 (WT). Thus, according to Table 3, V-Net alone exceeds the ensemble on ET and TC on the same validation set. The only coherent reconciliation is that Table 3 is not a held-out validation evaluation, but rather a cross-validation procedure that uses the validation labels for training or model selection. As written, the manuscript reports two incompatible evaluations on the same set, and this directly undermines the headline claim. The authors must clarify what Table 3 actually measures, whether validation labels were used, and provide a consistent evaluation protocol in which the ensemble is compared with individual models on an identical held-out split.
  2. [Abstract and Section 4.3, Table 2] The word 'significantly' is used without any supporting statistical evidence. The paper reports point estimates only, with no confidence intervals, standard deviations, or paired significance tests (e.g., Wilcoxon signed-rank or paired bootstrap) across the validation cases. For a medical segmentation claim, especially one framed as an improvement over individual models, this is load-bearing: the observed differences between the ensemble and V-Net in Table 2 are small (e.g., ET 0.8167 vs. 0.8021, TC 0.8358 vs. 0.8113), and without measures of variability the reader cannot judge whether the ensemble reliably outperforms a single model. The authors should add per-case distributions, CIs, or a paired test, or alternatively soften the claim to a descriptive comparison.
  3. [Section 4.3, Table 3 caption and text] The description of Table 3 is methodologically unclear and appears inconsistent. The caption says '5 Fold cross-validation of each model on the BraTS 2024 SSA Validation set,' and the text states the average scores were 'computed via the Synapse online platform.' A 5-fold cross-validation performed on the validation set implies training on parts of the validation set, which is not a held-out evaluation and would explain why Table 3's V-Net ET/TC scores exceed the ensemble's Table 2 scores. However, cross-validation results cannot be computed by the Synapse online platform, which evaluates a single submission on a fixed validation set. The authors need to specify the exact protocol: Were the validation labels used to train models in any fold? Was the Synapse platform used only for the Table 2 scores? Without this clarification, the integrity of the reported generalization estimates is in question.
  4. [Sections 3.4 and 4.3] The ensemble method is not fully specified. The authors state that STAPLE was used to merge the outputs of UNet3D, V-Net, and MSA-VNet, but they do not describe the input to STAPLE (hard label maps or probability maps), how the individual models' predictions were aligned, or whether any weighting or calibration was applied. Moreover, Table 3 lacks an ensemble row, so the cross-validation comparison does not include the ensemble. To support the claim that the ensemble improves over each individual model, the authors should report the ensemble's performance in the same cross-validation framework, along with the individual models, and provide sufficient implementation details for reproducibility.
minor comments (6)
  1. [Section 3.3.3] The text contains a duplicated phrase: 'We implemented MSA-VNet We implemented MSA-VNet (Multi-Scale Attention V-Net).' This should be corrected.
  2. [Section 4.2] There is a typo: 'BraTA-GLI dataset' should be 'BraTS-GLI dataset.'
  3. [Section 3.3.2] There is a typo: 'withing its residual blocks' should be 'within its residual blocks.'
  4. [Section 4.3, text after Table 3] The paragraph after Table 3 states 'The consistent superior performance of VNet across all tumor regions suggests that its multi-scale attention mechanism effectively enhances...' but V-Net does not use a multi-scale attention mechanism; MSA-VNet does. This appears to be an error in attribution and should be corrected.
  5. [Section 4.1] The reported environment uses Python 3.6.5 and PyTorch 1.8.0, which are quite old; consider noting whether this affects reproducibility or whether the code is expected to run in a modern environment.
  6. [References] Several references are incomplete or have inconsistent formatting (e.g., the BraTS-Africa challenge reference [13] lacks venue/page information, and some references use arXiv identifiers without journal details). The reference list should be cleaned up.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation is present: the ensemble result is an empirical benchmark claim, not a prediction reducible to fitted inputs, and the validation-set CV in Table 3 is an evaluation-integrity issue, not a circularity.

full rationale

The paper is an empirical engineering study, not a derivation, so there is no chain of equations in which a predicted quantity is defined from or fitted to the reported outcome. The central claim, that STAPLE ensembling of UNet3D, V-Net, and MSA-VNet improves Dice on the BraTS-SSA validation set, is checked against the external Synapse leaderboard (Table 2), and the constituent models are compared on the same leaderboard. Preprocessing (z-score normalization, 2nd/98th-percentile intensity stretching) and post-processing (removing ET connected components of 50 voxels or less) are taken from reference [26], but those are fixed, stated transformations, not parameters fitted to the evaluation labels, and they are not relabeled as predictions. The self-citations [7,12,13,15,16] provide dataset context and background; none is load-bearing for the ensemble result, and [13] is an external BraTS challenge dataset description. One anomaly is Section 4.3, which reports '5 Fold cross-validation of each model on the BraTS 2024 SSA Validation set' in Table 3; using validation labels in cross-validation would compromise the independence of Table 2, but the text does not exhibit a reduction of the final ensemble score to a fitted parameter, so this is an evaluation-integrity concern rather than a circular derivation. Section 3.1 also contains an unresolved citation placeholder '(cite)' and the paper lacks a stated validation-based model-selection protocol, but these are missing-support issues, not circularity. Therefore the circularity score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The headline results rest on the quality of public challenge labels, the transferability of models pretrained on BraTS-GLI, and the correctness of STAPLE fusion. These are domain assumptions, not proven facts. Two hand-chosen preprocessing and post-processing thresholds, one architecture modification, and a set of training hyperparameters are carried over from prior work or selected by hand, and no new entities are postulated.

free parameters (4)
  • post-processing connected component threshold = 50 voxels
    Section 3.5 removes ET connected components with volume 50 voxels or less. This hand-chosen threshold follows [26] and affects the final DICE scores.
  • voxel intensity rescaling percentiles = 2nd to 98th percentile
    Section 3.2 stretches voxel intensities from the 2nd to 98th percentile to increase contrast. This preprocessing choice is inherited from [26] and changes model inputs.
  • V-Net initial feature map count = 32
    Section 3.3.2 modifies V-Net by increasing feature maps from 16 to 32. This design choice affects model capacity and was not varied in experiments.
  • training hyperparameters = LR 6e-5, weight decay 1e-5, epochs 40, batch size 4
    Section 4.1 sets these values for all models. They are chosen by hand and may not be optimal for the SSA fine-tuning stage.
assumptions (5)
  • domain assumption BraTS-SSA ground truth segmentations are accurate enough to serve as training and validation labels.
    Section 3.1 describes labels generated by an initial nnU-Net segmentation and manual refinement by radiologists. If these labels contain systematic errors, all DICE comparisons are unreliable.
  • domain assumption Fine-tuning on 60 BraTS-SSA training cases after pre-training on BraTS-GLI transfers to the SSA validation distribution.
    Section 4.2 uses this two-stage training strategy. The SSA training set is small, so the transfer may fail on unseen hospitals or scanner types.
  • domain assumption STAPLE fusion of UNet3D, V-Net, and MSA-VNet outputs yields segmentations at least as accurate as the best individual model.
    Section 3.4 asserts the ensemble reduces bias and variance, but the paper does not show ensemble results on the local 12-case holdout, only on the validation leaderboard.
  • domain assumption The Synapse validation leaderboard is a fixed, independently held-out set whose metrics are unbiased.
    Section 4.3 relies entirely on platform-computed DICE and HD95. If the authors queried the leaderboard during development or selected methods based on it, scores are optimistically biased.
  • domain assumption The preprocessing and post-processing pipelines from [26] apply without modification to BraTS-SSA.
    Sections 3.2 and 3.5 adopt z-score normalization, percentile rescaling, and 50-voxel component removal from [26] without validating each choice on African data.

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Cite this review

Pith. "Pith review of Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings." pith.science (2026). https://pith.science/paper/GNH3CXXI

@misc{pith2026250202179,
  author       = {Pith},
  title        = {Pith review of: Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GNH3CXXI}},
  note         = {Machine review of arXiv:2502.02179}
}
read the original abstract

Segmentation of brain tumors is a critical step in treatment planning, yet manual segmentation is both time-consuming and subjective, relying heavily on the expertise of radiologists. In Sub-Saharan Africa, this challenge is magnified by overburdened medical systems and limited access to advanced imaging modalities and expert radiologists. Automating brain tumor segmentation using deep learning offers a promising solution. Convolutional Neural Networks (CNNs), especially the U-Net architecture, have shown significant potential. However, a major challenge remains: achieving generalizability across different datasets. This study addresses this gap by developing a deep learning ensemble that integrates UNet3D, V-Net, and MSA-VNet models for the semantic segmentation of gliomas. By initially training on the BraTS-GLI dataset and fine-tuning with the BraTS-SSA dataset, we enhance model performance. Our ensemble approach significantly outperforms individual models, achieving DICE scores of 0.8358 for Tumor Core, 0.8521 for Whole Tumor, and 0.8167 for Enhancing Tumor. These results underscore the potential of ensemble methods in improving the accuracy and reliability of automated brain tumor segmentation, particularly in resource-limited settings.

Figures

Figures reproduced from arXiv: 2502.02179 by the authors.

Figure 1
Figure 1. Unet3D architecture [28] 1 https://www.synapse.org/#!Synapse:syn51156910/wiki/622556 2 https://www.cameramriafrica.org/ 3 https://lacunafund.org/ 3 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. V-Net architecture [29] 3.3.3 Multi-Scale Attention VNet(MSA-VNet: We implemented MSA-VNet We implemented MSA-VNet (Multi-Scale Attention V-Net)[30], an advanced 3D convolutional neural network for volumetric medical image segmentation that builds upon V-Net principles. This architecture incorporates multi-scale attention mechanisms to enhance feature representation and segmentation accuracy. It consists of an encod… view at source ↗
Figure 3
Figure 3. Multi-Scale Attention-VNet architecture 3.5 Post-Processing Post-processing is a crucial step in glioma segmentation that occurs after the initial tumor segmentation. This step involves refining the initial results to enhance accuracy and minimize errors or inconsistencies in the final predictions. Techniques such as morphological operations, region growing, and level set evolution are often employed. Post￾processin… view at source ↗

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Reference graph

Works this paper leans on

32 extracted references · 29 canonical work pages

  1. [30]

    MSA-VNet: Multi-scale Attention-based V-Net for DCE-MRI Lesion Segmentation

    Chang Yan Xu, Zi Jiang Sang, and Ye Qin Shao. MSA-VNet: Multi-scale Attention-based V-Net for DCE-MRI Lesion Segmentation. In 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), pages 309–312, October 2022

    work page 2022

  2. [1]

    Brain tumor segmentation with deep neural networks

    Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, and Hugo Larochelle. Brain tumor segmentation with deep neural networks. Med. Image Anal., 35:18–31, January 2017

    work page 2017

  3. [2]

    Review of MRI-based brain tumor image segmentation using deep learning methods

    Ali I¸ sın, Cem Direko˘glu, and Melike ¸ Sah. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci., 102:317–324, 2016

    work page 2016

  4. [3]

    Deep Learning for Brain Tumor Segmentation: A Survey of State-of- the-Art

    Tirivangani Magadza and Serestina Viriri. Deep Learning for Brain Tumor Segmentation: A Survey of State-of- the-Art. Journal of Imaging, 7(2):19, January 2021

    work page 2021

  5. [4]

    Drrnet: dense residual refine networks for automatic brain tumor segmentation

    Jiawei Sun, Wei Chen, Suting Peng, and Boqiang Liu. Drrnet: dense residual refine networks for automatic brain tumor segmentation. Journal of medical systems, 43:1–9, 2019

    work page 2019

  6. [5]

    Adult brain tumors in Sub-Saharan africa: A scoping review

    Ulrick Sidney Kanmounye, Claire Karekezi, Arsene Daniel Nyalundja, Ahmed K Awad, Tsegazeab Laeke, and James A Balogun. Adult brain tumors in Sub-Saharan africa: A scoping review. Neuro. Oncol., 24(10):1799–1806, October 2022

    work page 2022

  7. [6]

    Status of magnetic resonance imaging systems and quality control programs in nigeria

    Maruf Adewole, Taofeeq A Ige, Nicholas Irurhe, Philip Adewole, Michael Akpochafor, Ayo Ibitoye, and Samuel Adeneye. Status of magnetic resonance imaging systems and quality control programs in nigeria. medRxiv, pages 2023–06, 2023

    work page 2023

  8. [7]

    Ai for population and global health in radiology, 2022

    Udunna C Anazodo, Maruf Adewole, and Farouk Dako. Ai for population and global health in radiology, 2022

    work page 2022

Show all 32 references
  1. [8]

    Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric u-net and asymmetric residual-blocks

    Mahmoud Khaled Abd-Ellah, Ali Ismail Awad, Ashraf AM Khalaf, and Amira Mofreh Ibraheem. Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric u-net and asymmetric residual-blocks. Scientific reports, 14(1):9501, 2024

    2024

  2. [9]

    U-net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part ...

    2015

  3. [10]

    Somasundaram and R

    S. Somasundaram and R. Gobinath. Current Trends on Deep Learning Models for Brain Tumor Segmentation and Detection – A Review. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pages 217–221, Faridabad, India, February 2019. IEEE

    2019

  4. [11]

    Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024

    Yuhao Huang, Xin Yang, Lian Liu, Han Zhou, Ao Chang, Xinrui Zhou, Rusi Chen, Junxuan Yu, Jiongquan Chen, Chaoyu Chen, et al. Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024

    2024

  5. [13]

    Maruf Adewole, Jeffrey D. Rudie, Anu Gbadamosi, Oluyemisi Toyobo, Confidence Raymond, Dong Zhang, Olubukola Omidiji, Rachel Akinola, Mohammad Abba Suwaid, Adaobi Emegoakor, Nancy Ojo, Kenneth Aguh, Chinasa Kalaiwo, Gabriel Babatunde, Afolabi Ogunleye, Yewande Gbadamosi, Kator ...

    2023

  6. [14]

    Optimized U-Net for Brain Tumor Segmentation, December 2021

    Michał Futrega, Alexandre Milesi, Michal Marcinkiewicz, and Pablo Ribalta. Optimized U-Net for Brain Tumor Segmentation, December 2021. arXiv:2110.03352 [cs, eess]

    2021 arXiv

  7. [15]

    Mohannad Barakat, Noha Magdy, Jjuuko George William, Ethel Phiri, Raymond Confidence, Dong Zhang, and Udunna C. Anazodo. Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations, December 2023. arXiv:2312.11775 [cs, eess]

    2023 arXiv

  8. [16]

    Bringing mri to low-and middle-income countries: directions, challenges and potential solutions

    Sanjana Murali, Hao Ding, Fope Adedeji, Cathy Qin, Johnes Obungoloch, Iris Asllani, Udunna Anazodo, Ntobeko AB Ntusi, Regina Mammen, Thoralf Niendorf, et al. Bringing mri to low-and middle-income countries: directions, challenges and potential solutions. NMR in Biomedicine, pa...

    2023

  9. [17]

    Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks

    Muhammad Imran Razzak, Muhammad Imran, and Guandong Xu. Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE journal of biomedical and health informatics, 23(5):1911–1919, 2018. 8

    1911

  10. [18]

    The emerging roles of artificial intelligence in cancer drug development and precision therapy

    Guosheng Liang, Wenguo Fan, Hui Luo, and Xiao Zhu. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomedicine & Pharmacotherapy, 128:110255, 2020

    2020

  11. [19]

    Segmentation of organs-at-risks in head and neck ct images using convolutional neural networks

    Bulat Ibragimov and Lei Xing. Segmentation of organs-at-risks in head and neck ct images using convolutional neural networks. Medical physics, 44(2):547–557, 2017

    2017

  12. [20]

    Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

    Tim Lustberg, Johan van Soest, Mark Gooding, Devis Peressutti, Paul Aljabar, Judith van der Stoep, Wouter van Elmpt, and Andre Dekker. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiotherapy and Oncology, 126(2):312–317, 2018

    2018

  13. [21]

    Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy

    Price Jackson, Nicholas Hardcastle, Noel Dawe, Tomas Kron, Michael S Hofman, and Rodney J Hicks. Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy. Frontiers in oncology, 8:215, 2018

    2018

  14. [22]

    Automatic 3d liver segmentation based on deep learning and globally optimized surface evolution

    Peijun Hu, Fa Wu, Jialin Peng, Ping Liang, and Dexing Kong. Automatic 3d liver segmentation based on deep learning and globally optimized surface evolution. Physics in Medicine & Biology, 61(24):8676, 2016

    2016

  15. [23]

    Combining deep learning with anatomical analysis for segmentation of the portal vein for liver sbrt planning

    Bulat Ibragimov, Diego Toesca, Daniel Chang, Albert Koong, and Lei Xing. Combining deep learning with anatomical analysis for segmentation of the portal vein for liver sbrt planning. Physics in Medicine & Biology, 62(23):8943, 2017

    2017

  16. [24]

    A hybrid attention-based residual unet for semantic segmentation of brain tumor

    Wajiha Rahim Khan, Tahir Mustafa Madni, Uzair Iqbal Janjua, Umer Javed, Muhammad Attique Khan, Majed Alhaisoni, Usman Tariq, and Jae-Hyuk Cha. A hybrid attention-based residual unet for semantic segmentation of brain tumor. Computers, Materials and Continua, 76(1):647–664, 2023

    2023

  17. [25]

    Automated ensemble- based segmentation of adult brain tumors: A novel approach using the brats africa challenge data

    Chiranjeewee Prasad Koirala, Sovesh Mohapatra, Advait Gosai, and Gottfried Schlaug. Automated ensemble- based segmentation of adult brain tumors: A novel approach using the brats africa challenge data. arXiv preprint arXiv:2308.07214, 2023

    2023 arXiv

  18. [26]

    An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation, February 2024

    Tianyi Ren, Ethan Honey, Harshitha Rebala, Abhishek Sharma, Agamdeep Chopra, and Mehmet Kurt. An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation, February 2024. arXiv:2402.07008 [cs, eess]

    2024 arXiv

  19. [27]

    Extending nn-unet for brain tumor segmentation, 2021

    Huan Minh Luu and Sung-Hong Park. Extending nn-unet for brain tumor segmentation, 2021

    2021

  20. [28]

    Lienkamp, Thomas Brox, and Olaf Ronneberger

    Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger. 3d u-net: Learning dense volumetric segmentation from sparse annotation, 2016

    2016

  21. [29]

    V-net: Fully convolutional neural networks for volumetric medical image segmentation, 2016

    Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation, 2016

    2016

  22. [31]

    Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods

    Ali I¸ sın, Cem Direko˘glu, and Melike ¸ Sah. Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods. Procedia Computer Science, 102:317–324, 2016

    2016

  23. [32]

    Brain tumor segmentation in multi-parametric magnetic resonance imaging using model ensembling and super-resolution, 07 2022

    Zhifan Jiang, Can Zhao, Xinyang Liu, and Marius George Linguraru. Brain tumor segmentation in multi-parametric magnetic resonance imaging using model ensembling and super-resolution, 07 2022

    2022

  24. [33]

    Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation

    Simon K Warfield, Kelly H Zou, and William M Wells. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging, 23(7):903–921, July 2004. 9

    2004

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Reviewed August 9, 2026 · model on record in the stance chip above.