Automated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks

Indrajit Mazumdar

REVIEW 4 major objections 6 minor 37 references

Automated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A 2D-only ensemble of three view-specific U-Nets, averaged at inference, matches 3D networks on brain tumour segmentation while using far less memory and time.

desk verdict A credible, useful BraTS 2018 write-up whose central interpretive claim—multi-view ensembling captures 3D context—is confounded with ensembling itself and contradicted by one of its own tables. read the letter →

arxiv 1908.04250 v3 pith:JKTNM3U7 submitted 2019-08-12 eess.IV cs.CV

Indrajit Mazumdar This is my paper · ORCID

classification eess.IVcs.CV

keywords braintumoursegmentationglioma2DU-NetfullyresidualconvolutionalnetworkorthogonalviewsensembleweightedDicelossBraTS2018Hausdorffdistance

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's thesis is that 3D context, normally the reason to use 3D convolutional networks, can be supplied to a much cheaper 2D pipeline by ensembling three view-specific networks. The author trains the same fully residual U-Net on axial, sagittal, and coronal slices and averages their softmax outputs at inference, reaching Dice scores of 0.79, 0.90, and 0.82 for enhancing tumour, whole tumour, and tumour core on the BraTS 2018 validation set. These numbers sit beside recent 3D results in the paper's comparison table, which supports the claim that a 2D network with multi-view context can be comparable to a 3D network. The practical payoff is that automated tumour segmentation could run with much lower memory and time costs. The paper also reports that a single 2D network trained on mixed-view patches fails to capture the same context, and that adding a fourth level to the U-Net hurts performance.

What carries the argument

The central object is a 2D fully residual U-Net. Its encoder and decoder are built from residual blocks, each containing two 3×3 convolutions with batch normalisation and ReLU; downsampling uses stride-2 convolutions, upsampling uses bilinear interpolation, and skip connections concatenate encoder activations to the corresponding decoder level. Around this backbone sits the decisive mechanism: an ensemble of three such networks trained separately on axial, sagittal, and coronal slices, with predictions obtained by averaging the three softmax outputs. The loss is a weighted Dice loss, $w_c = 1/\sum_i g_{ci}$, which gives more weight to small tumour classes so that a single network can handle the severe class imbalance. The ensemble is what lets a 2D model see volume context without 3D convolutions.

What would settle it

Train a 3D version of this exact residual U-Net on the same BraTS 2018 training and validation splits with the same loss, augmentation, and training schedule, and compare it with the three-view 2D ensemble; if the 3D model clearly beats the ensemble on Dice or Hausdorff distance, the central claim would be undermined. A smaller test is to train a single 2D network on mixed-view patches with more epochs and see whether it can match the ensemble, which would challenge the claim that view-specific ensembles are necessary.

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Extended reading notes

Core claim

On the paper's own account, the discovery is that 3D contextual information cannot be captured by one 2D network trained on patches from multiple views, but can be captured by an ensemble of three 2D networks each trained on a single orthogonal view. The ensemble improves both Dice scores and the 95% Hausdorff distances, which drop from 7.29, 10.39, and 11.60 mm in the axial baseline to 2.99, 6.28, and 5.90 mm for enhancing tumour, whole tumour, and tumour core respectively. The author reads this as evidence that a 2D network combining axial, sagittal, and coronal context can deliver improvements comparable to 3D networks while remaining simpler and less memory-intensive.

Load-bearing premise

The load-bearing assumption is that differences between the proposed method and the published 3D methods on the BraTS 2018 validation leaderboard can be attributed to the 2D-versus-3D design, even though those 3D scores were produced by other teams with different architectures, ensembling strategies, training budgets, and tuning, and the paper does not train a 3D version of its own network.

Editorial extensions

If this is right

A 2D-only pipeline can reach Dice scores comparable to published 3D networks on the BraTS 2018 validation set, so 3D convolutions are not strictly necessary for competitive glioma segmentation.
Mixing axial, sagittal, and coronal slices into one network's training set is not enough to capture 3D context; the improvement requires separate view-specific models whose outputs are averaged.
The weighted Dice loss with inverse-frequency weights lets a single model handle the extreme class imbalance, where roughly 98.88% of all voxels are background.
A four-level U-Net underperforms a three-level version, so an optimal U-depth exists and must be chosen rather than maximised.
The three-view ensemble gives 95% Hausdorff distances of 2.99, 6.28, and 5.90 mm for enhancing tumour, whole tumour, and tumour core, showing that the gain is not only overlap but boundary shape.

Reading between the lines

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

If the benefit comes from averaging view-consistent predictions, the same ensemble design should transfer to other 2D segmentation architectures and to other imaging settings where slice spacing is anisotropic.
Because the ensemble mainly improves Hausdorff distance rather than Dice, the mechanism probably cleans boundary errors; an ablation that measures border error separately could confirm this reading.
A direct runtime and peak-memory comparison of the 2D ensemble against a 3D U-Net on the same GPU would turn the paper's cost advantage into a quantitative claim, since the paper reports accuracy but leaves runtime unmeasured.
The optimum-depth finding suggests that depth should be tuned per view rather than once globally, a variation the paper leaves untested.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a straightforward BraTS 2018 write-up with credible official-tool scores, but the headline interpretation—that orthogonal views provide 3D context beyond simple ensembling—is not supported by the experiments as reported, and one of the paper's own tables contradicts the text on the single-multiview baseline.

What's genuinely useful: the paper records a clean empirical comparison between a single 2D U-Net trained on all three views and an ensemble of view-specific 2D U-Nets, on an external held-out validation set. The ensemble numbers (Dice 0.79/0.90/0.82, HD 2.99/6.28/5.90) are plausibly competitive, and the practical motivation—2D nets are cheap and simple—is legitimate. The depth experiment, though only one increment, honestly reports a negative result that other practitioners may find informative.

The soft spots are real. The central causal claim conflates number of models with view diversity: there is no axial-only ensemble control. Averaging three models alone could easily explain the large Hausdorff improvements. Second, Section 3.2 states that the single multi-view model increased Hausdorff distance for all three sub-regions, but Table 1 (training) shows HD decreasing for all three, and Table 2 (validation) shows TC improving from 11.60 to 7.22. That's a direct internal contradiction, and it undermines the conclusion that a single 2D network cannot capture 3D context. Third, the comparison to 3D methods is against other teams' leaderboard entries, not a same-architecture 3D baseline, so the 'comparable to 3D' claim is not isolated from training budget, ensembling, or tuning. There are no error bars anywhere.

This is a modest, honest paper with addressable flaws. A serious editor should send it to review, but the reviewers should ask for the ensemble control, a corrected reading of the tables, and ideally uncertainty quantification or code. It's not a wasted read for anyone working on efficient segmentation or challenge evaluation design.

Referee Report

4 major / 6 minor

Summary. The paper proposes a 2D fully residual U-Net with a weighted Dice loss for brain tumour segmentation on the BraTS 2018 dataset. The authors report two methods for incorporating 3D context: a single network trained on patches from axial, sagittal, and coronal views, and an ensemble of three networks each trained on one orthogonal view with averaged softmax outputs. They report validation Dice scores of 0.79 (ET), 0.90 (WT), and 0.82 (TC) and Hausdorff distances of 2.99, 6.28, and 5.90, and argue that the ensemble of three 2D networks can capture 3D context and deliver performance comparable to 3D CNN methods. They also report that increasing U-Net depth from 3 to 4 decreases performance and that a single multi-view 2D network cannot capture 3D context.

Significance. If the central claims were fully supported, this would be a practically useful result: a simple 2D ensembling strategy that matches 3D CNNs on BraTS with lower memory and time costs would be of interest to the medical imaging community. The paper's strengths include the use of the official BraTS 2018 validation tool for measuring Dice and Hausdorff distances, a clear description of the architecture and training procedure, and a comparison against several published 3D methods. The weighted Dice loss is standard but appropriately motivated by the extreme class imbalance. However, the experimental design does not currently isolate the causal factors behind the ensemble's improvement, and the comparison to 3D methods is based on uncontrolled leaderboard scores. The central claims are therefore plausible but not established as stated.

major comments (4)

[Sections 2.6 and 3.2, Tables 1 and 2] The comparison between "Baseline" (a single axial-view model) and "Ensemble of three models" changes two variables simultaneously: the number of networks averaged and the diversity of views. The large improvements in 95% Hausdorff distance on the validation set (ET 7.29 to 2.99, WT 10.39 to 6.28, TC 11.60 to 5.90) could plausibly be an ensembling effect alone, because averaging independent models is known to reduce variance and outlier errors. To support the claim that orthogonal views provide 3D contextual information beyond simple ensembling, the authors should add a control ensemble of three axial-only networks. Without this control, the central attribution of the ensemble gain to multi-view context is not established.
[Section 3.2, text versus Tables 1 and 2] The statement that training a single model on three orthogonal views "resulted in little improvement in Dice score for the enhancing tumour but the Hausdorff distance increased for all the three tumour sub-regions" is contradicted by the paper's own tables. In Table 1, all three Hausdorff distances decrease compared with the baseline (5.71 to 4.96, 11.94 to 8.31, 9.71 to 7.29), and in Table 2 the TC Hausdorff distance decreases from 11.60 to 7.22. The abstract and Section 4 repeat the stronger claim that a single 2D network "cannot capture 3D contextual information," but the reported results are mixed. The text should be corrected and the conclusion re-framed to match the actual data.
[Section 3.2, Table 4] The claim that a 2D network "can deliver comparable performance to a 3D network" rests on a comparison with leaderboard scores from other teams' 3D architectures. These methods differ in architecture, training data usage, ensembling, post-processing, and compute budget, and the paper does not train a 3D version of its own network. The score gaps in Table 4 (e.g., ET Dice 0.79 versus 0.80-0.82, TC Dice 0.82 versus 0.84-0.86) are small, but without controlling for the confounds, the results do not demonstrate that the 2D-versus-3D design choice, rather than other factors, explains the comparable performance. Please either add an internal 3D baseline or explicitly present Table 4 as an uncontrolled benchmark reference, and soften the causal wording.
[Section 3.2, Tables 1 and 2] Every configuration appears to be evaluated from a single training run, with no variance estimates or statistical tests. This is particularly problematic for the paper's secondary findings: the depth conclusion is based on a single increment (depth 3 to depth 4), and the differences between the single multi-view model and the baseline are mixed (some metrics improve, some worsen). The authors should report multiple-run statistics or clearly acknowledge that the observed differences may not be significant, especially when making claims such as "we must always use an optimum depth."

minor comments (6)

[Section 2.1] The 80/20 split of extracted patches appears to be performed at the slice level rather than the patient level, so patches from the same patient can appear in both the training and internal validation sets. Please state whether the split was patient-independent or discuss the potential impact on parameter tuning.
[Section 3.2] The paper claims that the 2D method "consumes very less time and memory" compared with 3D methods, but no quantitative timing or memory measurements are reported. Please provide actual inference time and GPU memory usage if this advantage is to be considered a contribution.
[Figure 1 caption] The caption reads "On the top of each box the number channels are denoted"; this should be "the number of channels are denoted" or similar.
[Section 3.2, final paragraph] The text describing Figure 4 says "green colour represents the ET," but the caption correctly says green represents ED. This appears to be a typo and should be corrected.
[References] References [13] and [14] contain malformed parentheses: "ISLES-MICCAI. (, 2015" and "(IEEE, 2015" respectively. Please fix these formatting errors.
[Section 2.6] It is unclear whether the single multi-view model is trained by concatenating all patches from all views into one training file or by alternating views per epoch. Please clarify the exact training protocol.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the paper's claims are empirical and benchmarked against an external held-out validation set.

full rationale

The paper makes no derivation that reduces to its own inputs. The central quantitative claims are Dice scores and Hausdorff distances on the BraTS 2018 validation set obtained through the official online evaluation tool, and the comparison to 3D state-of-the-art methods uses published leaderboard results that are external to this paper. The weighted Dice loss weights in Eq. (1) are fixed inverse-frequency weights computed from the training-set ground truth, not parameters fitted to the validation outcome; the loss is a standard training objective, not a disguised prediction. The depth experiment, the single multi-view model, and the three-view ensemble are all empirical ablations reported in Tables 1 and 2 rather than quantities forced by construction. There are no self-citations invoked as load-bearing evidence, no uniqueness theorem imported from prior work by the same author, and no known result renamed as a new contribution. The possible confound that the ensemble gain over the axial baseline could be partly an ensembling effect rather than evidence of 3D contextual information is a validity and experimental-design concern, not a circularity, because the comparison is against real external data and does not presuppose its conclusion. Under the rule that circularity requires a quoted reduction of a claimed result to its inputs, no such reduction is present, so the appropriate finding is no significant circularity.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities and makes no first-principles derivation. It relies on standard deep learning hyperparameters (filters, learning rate, depth, etc.), some chosen by hand and the U-Net depth chosen from an ablation. The main assumptions concern the validity of the BraTS benchmark, the sufficiency of the four MRI modalities, and the generalization from training to validation patients.

free parameters (7)

U-Net depth = 3
Set to 3 after comparing with depth 4 on training and validation sets (Tables 1 and 2); the choice is driven by observed performance.
Initial number of filters = 32
Architecture choice stated in Section 2.2; not justified by a search.
Learning rate = 1e-4
Adam optimizer setting in Section 2.3; standard choice.
L2 regularization strength = 1e-5
Section 2.3; standard small value.
Batch size = 8
Section 2.3; limited by GPU memory.
Number of epochs = 300
Section 2.3; chosen to allow convergence.
Patch size = 128x128
Section 2.1; fixed slice size after extraction, affects context available to the 2D networks.

assumptions (4)

domain assumption BraTS 2018 ground-truth labels and the online validation tool provide an unbiased measurement of segmentation performance.
All quantitative conclusions depend on the correctness of the manual segmentations and the evaluation system (Section 3.1).
domain assumption The four MRI modalities (T1, T1ce, T2, FLAIR) contain sufficient information to delineate the tumour sub-regions after the described preprocessing.
The method uses only these modalities and 2D slices; this is a standard assumption in BraTS papers (Section 2.1).
domain assumption Training on the BraTS training set generalizes to the BraTS validation distribution.
The network is trained on 285 patients and evaluated on 66 unseen patients; the challenge design assumes distributional match (Section 3.1).
domain assumption Dice score and 95% Hausdorff distance are appropriate and sufficient metrics for the clinical goal.
The paper adopts the challenge metrics without discussing their limitations (Section 3.2).

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Cite this review

Pith. "Pith review of Automated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks." pith.science (2026). https://pith.science/paper/JKTNM3U7

@misc{pith2026190804250,
  author       = {Pith},
  title        = {Pith review of: Automated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JKTNM3U7}},
  note         = {Machine review of arXiv:1908.04250}
}

read the original abstract

Automated brain tumour segmentation has the potential of making a massive improvement in disease diagnosis, surgery, monitoring and surveillance. However, this task is extremely challenging. Here, we describe our automated segmentation method using 2D CNNs that are based on U-Net. To deal with class imbalance effectively, we have used a weighted Dice loss function. We found that increasing the depth of the 'U' shape beyond a certain level results in a decrease in performance, so it is essential to choose an optimum depth. We also found that 3D contextual information cannot be captured by a single 2D network that is trained with patches extracted from multiple views whereas an ensemble of three 2D networks trained in multiple views can effectively capture the information and deliver much better performance. We obtained Dice scores of 0.79 for enhancing tumour, 0.90 for whole tumour, and 0.82 for tumour core on the BraTS 2018 validation set. Our method using 2D network consumes very less time and memory, and is much simpler and easier to implement compared to the state-of-the-art methods that used 3D networks; still, it manages to achieve comparable performance to those methods.

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Reference graph

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