Pith. sign in

REVIEW 4 major objections 4 minor 42 references

Deep Learning-Based Automatic Delineation of Liver Domes in kV Triggered Images for Online Breath-hold Reproducibility Verification of Liver Stereotactic Body Radiation Therapy

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A deep-learning pipeline can delineate the liver dome on kV triggered images in under one second, with a reported error of 6.4–7.7 mm RMSE against manual contours and detection rates of 92% and 76% in two patient folds.

desk verdict A sensible proof-of-principle for an underexplored clinical task, but the headline RMSE numbers are not defined tightly enough to verify without clarification. read the letter →

arxiv 2411.15322 v1 pith:KEBSUV76 submitted 2024-11-22 physics.med-ph cs.CV

Sugandima Weragoda , Ping Xia , Kevin Stephans , Neil Woody , Michael Martens , Robert Brown , Bingqi Guo This is my paper · ORCID
classification physics.med-phcs.CV
keywords liverSBRTbreath-holdreproducibilitykVtriggeredimagingdomedelineationdeeplearningsegmentationU-NetHEDedgedetectiononlineimageguidance
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-learning pipeline can replace manual visual checks of liver dome position during breath-hold liver SBRT. It assembles a U-Net with transfer learning and a pretrained edge-detection model to segment and then trace the liver dome on 711 kV-triggered images from 24 patients. Reported results are fast (under one second per image) and moderately accurate (6.4–7.7 mm RMSE versus manual contours, with detection rates of 92% and 76% in the two cross-validation folds). The authors position the work as a proof of principle, not yet a clinically validated tool, and say further improvement in accuracy and detection rate is needed before clinical use. If the approach holds up, it would enable automatic, objective breath-hold verification and eventually automatic beam gating in liver SBRT.

What carries the argument

The load-bearing mechanism is the combination of a U-Net (an encoder-decoder convolutional network with skip connections, here with a frozen pretrained encoder) that predicts a liver-dome-region mask, and a hand-designed postprocessing chain that converts the mask into a dome contour: binary thresholding at mean intensity, largest connected component, HED edge detection, edge thinning, and morphological erosion and dilation with custom kernels to isolate the upper boundary. A three-channel input (original image, contrast-enhanced image, and HED edge map) supplies the network with the soft-tissue contrast and edge information that planar kV images lack.

What would settle it

Have a second radiation oncologist independently re-contour the liver domes on the same 711 images. If the inter-observer RMSE between the two manual contour sets is comparable to or larger than the 6.4–7.7 mm RMSE reported here, then the pipeline's agreement with ground truth is within the noise of manual delineation rather than an independent measure of anatomical accuracy.

Watch

Extended reading notes

Core claim

The paper claims that a pipeline consisting of a U-Net trained to segment the liver dome region, followed by thresholding, extraction of the largest connected component, HED edge detection, edge thinning, and morphological filtering, can automatically delineate the liver dome on planar kV-triggered images. In two-fold cross-validation on 711 images from 24 patients, the model's liver-region predictions reached IoU above 0.9 for every patient, the end-to-end delineation ran in under one second per image, and the extracted dome contours differed from manual contours by 6.4±1.6 mm (Fold 1, detection rate 91.7%) and 7.7±2.3 mm (Fold 2, detection rate 76.3%).

Load-bearing premise

The manual liver dome contours from the earlier study are accurate enough to serve as ground truth, and expanding each one inferiorly by 20 pixels yields a valid liver-region training mask; if either fails, the IoU scores and RMSE values lose their meaning.

Editorial extensions

If this is right

  • Automatic liver dome delineation in under one second per image makes real-time online breath-hold verification feasible during treatment.
  • Replacing manual visual comparison with an automatic contour could reduce subjectivity and human intervention in breath-hold checks.
  • The reported 6.4–7.7 mm RMSE is within the range needed to catch breath-holds with more than 10 mm deviation, but the authors state that the accuracy and detection rate must improve before the technique is clinically relevant.
  • Two-fold cross-validation with patient-level splits suggests the pipeline generalizes across patients at the same institution, while multi-institutional testing remains an open step.
  • The pipeline could serve as the baseline for the first deep-learning-based liver dome delineation on kV planar images.

Reading between the lines

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

  • If the same pipeline were trained on diaphragm or other organ-at-risk masks, it could extend to breath-hold verification for lung or abdominal SBRT, since the method itself is not liver-specific.
  • The paper's observation that augmentation beyond 500 images degraded performance suggests the synthetic transformations did not match real image variation; richer augmentation or multi-institutional data might improve the lower detection rate in Fold 2.
  • Combining the predicted dome contour with the planning-CT dome position would enable automatic beam gating, which the paper names as the eventual clinical goal.
  • A failure analysis separating segmentation errors from postprocessing errors, as hinted at for patients 03, 14, and 19, could pinpoint whether the 76% detection rate in Fold 2 is due to anatomy, image quality, or the morphological step.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. This proof-of-principle study proposes an automatic pipeline for delineating the liver dome in kV-triggered planar images acquired during breath-hold verification for liver SBRT. The pipeline consists of a U-Net with a VGG16 encoder pretrained on ImageNet to segment a liver region, followed by thresholding, largest-connected-component extraction, HED edge detection, Canny edge detection, and morphological operations with custom kernels to extract the liver dome contour. The dataset comprises 711 kV-triggered images from 24 patients, split into two patient-level folds for two-fold cross-validation. The U-Net is trained on augmented data (500 images per fold) with early stopping. The authors report IoU > 0.9 for liver region segmentation for all patients, and for the final pipeline, an RMSE of 6.4±1.6 mm (Fold 1) and 7.7±2.3 mm (Fold 2), with detection rates of 91.7% and 76.3%, respectively. Training takes about 30 minutes and inference under one second. The conclusion is that the pipeline is a clinically feasible proof-of-principle for automatic liver dome delineation.

Significance. If the reported accuracy is reproducible, the study makes a useful contribution: it is, to the authors' knowledge, the first DL-based liver dome delineation from kV planar images, and it demonstrates a novel use of HED edge detection in postprocessing. The patient-level two-fold cross-validation is an appropriate design for generalization assessment, and the reported IoU values are consistently high. The fast inference time and modest training resource requirement are strengths for potential online clinical use. However, the paper's headline accuracy figures (RMSE and detection rate) rest on an incompletely specified evaluation protocol and an undescribed postprocessing step, so the central quantitative claims cannot currently be independently verified. The ground-truth mask construction also relies on an unvalidated 20-pixel inferior expansion. These issues are fixable and do not invalidate the feasibility concept, but they do prevent acceptance in the present form.

major comments (4)
  1. [Evaluation] The definition of the RMSE metric is incomplete. The text (Evaluation section, p. 14) states only that RMSE is computed 'between the predicted and ground truth contours,' without specifying (i) whether images in which the postprocessing yields no dome or a too-sparse dome are excluded from the RMSE average, and what value is assigned if they are included; (ii) how the predicted contour is matched to the manual contour or how points are resampled for the pointwise computation; and (iii) the physical pixel spacing or magnification factor used to convert pixel distances to millimeters. Because detection rates are 91.7% and 76.3%, the RMSE may be computed on a favorable subset of images, which would not represent the accuracy on all triggered images. These details must be provided before the reported 6.4±1.6 mm and 7.7±2.3 mm values can be interpreted.
  2. [Postprocessing] The postprocessing step is not reproducible as described. The paper (Postprocessing, p. 14) refers to 'morphological postprocessing with custom kernels' and 'erosion operations with custom kernels' without specifying the kernel sizes, shapes, number of iterations, or the interpolation method used to connect contour discontinuities. Without this information, the final dome extraction cannot be replicated by another group, and the reported RMSE and detection rates are not independently verifiable. Please provide the exact morphological recipe and algorithm.
  3. [Image preprocessing and ground truth labeling] The training masks for the U-Net are created by expanding the manual liver dome contours inferiorly by 20 pixels (Image preprocessing and ground truth labeling, p. 10). This expansion is asserted to form a 'liver region' mask, but no justification or sensitivity analysis is provided. If the 20-pixel expansion does not match the true liver region (for example, because it encroaches on other anatomy or is too narrow), both the IoU scores and the contours extracted in postprocessing—and hence the final RMSE—are affected. Please validate this assumption, e.g., by comparing the expanded masks with an independent liver region reference or by varying the expansion width and reporting the impact on final metrics.
  4. [Evaluation] The detection-rate metric is not fully specified. The Evaluation section (p. 14) defines a dome as undetectable when the 'resulting dome contour ... is non-existent or too sparse,' but 'too sparse' is never quantified. This subjective threshold can materially change the reported 91.7% and 76.3% detection rates. Please define an operational criterion (e.g., minimum number of contour points, minimum contour length, or minimum fraction of the ground-truth contour length).
minor comments (4)
  1. [Figure 4] The figure caption reads 'Postprocessing seps' but should read 'Postprocessing steps'.
  2. [Discussion] In the Discussion (p. 19), the sentence 'In both cases, good learning and generalizing of the models can be observed because the validation learning curves remain higher than the training curves' is unclear: a validation loss that remains above the training loss is typically a sign of some overfitting, not necessarily 'good' generalization. Please clarify the intended interpretation.
  3. [Results] In the Results (p. 17), the statement 'For patients 1 to 12, comprising Fold1, the mean divergence ranged from 4.1 mm to 9.6 mm' does not specify which model (trained on Fold2 or Fold1) produced these values; clarify the fold-to-model mapping.
  4. [Figure 7(a)] Figure 7(a) marks outliers with circles, but no formal outlier definition is given in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the pipeline is evaluated by two-fold cross-validation against fixed manual contours from a prior study, with no test-set fitting.

full rationale

The derivation chain is supervised learning from fixed manual ground truth, not a self-referential argument. The dataset and manual contours come from a previously published study [1]; this is a self-citation by overlapping authors, but the contours serve as fixed external labels rather than as a conclusion derived from this paper. Two-fold cross-validation splits by patient, so the U-Net and postprocessing parameters for each test fold are trained without access to that fold's labels; the reported IoU and RMSE are computed on held-out patients. Pretrained components (ImageNet VGG16 encoder, HED) are public and not fitted to the target data. The final liver dome is extracted from the predicted region by thresholding, connected-component selection, edge detection, and morphology; the evaluation compares it to manual contours, so the metric is not defined in terms of the prediction itself. The manuscript does note limitations (small single-institution dataset, internal 10% validation split, augmentation plateau), and the Evaluation section leaves the RMSE protocol underspecified (e.g., whether undetected domes are excluded, pixel-to-mm conversion, contour correspondence). Those are reproducibility/verifiability concerns, not circularity: no equation reduces to its own input, and no fitted parameter is renamed as a prediction. Conservative score 0.

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

The central feasibility claim depends mainly on ground-truth label construction and transfer-learning assumptions. The 20-pixel mask expansion and the per-image threshold are the most consequential hand-set parameters. No new physical entities are introduced.

free parameters (4)
  • Dropout rates in decoder blocks 1-4 = 0.4, 0.2, 0.2, 0.1
    Hand-chosen to reduce overfitting on the small training set; not optimized or derived.
  • Ground truth mask inferior expansion = 20 pixels
    Ad hoc expansion of manual dome contours to create liver region training labels; directly shapes IoU and final dome extraction.
  • Binary postprocessing threshold = per-image mean intensity
    Prediction maps are thresholded at the image mean before largest connected component extraction; threshold is set per image, not tuned on a validation set.
  • Training hyperparameters = learning rate 0.001, batch size 20, 100 epochs, early stopping at validation loss 0.1
    Standard choices; early stopping criterion and augmentation count (500 images) are chosen by hand.
assumptions (4)
  • domain assumption Pretrained VGG16 (ImageNet) and HED features transfer to low-contrast kV planar X-ray images.
    The pipeline relies on transfer learning; no evidence is given that ImageNet features are optimal for this imaging modality.
  • domain assumption Manual liver dome contours from the prior study are accurate ground truth.
    No inter-observer variability, consensus process, or independent validation of the manual contours is reported.
  • domain assumption The liver dome position in a 2D kV image is a valid surrogate for breath-hold reproducibility.
    This clinical rationale is inherited from reference [1] and is not tested in the current study.
  • ad hoc to paper Expanding the manual liver dome contour inferiorly by 20 pixels yields a valid liver region mask.
    The 20-pixel expansion is a study-specific, unvalidated choice that defines the segmentation target.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Deep Learning-Based Automatic Delineation of Liver Domes in kV Triggered Images for Online Breath-hold Reproducibility Verification of Liver Stereotactic Body Radiation Therapy." pith.science (2026). https://pith.science/paper/KEBSUV76

@misc{pith2026241115322,
  author       = {Pith},
  title        = {Pith review of: Deep Learning-Based Automatic Delineation of Liver Domes in kV Triggered Images for Online Breath-hold Reproducibility Verification of Liver Stereotactic Body Radiation Therapy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KEBSUV76}},
  note         = {Machine review of arXiv:2411.15322}
}
read the original abstract

Stereotactic Body Radiation Therapy (SBRT) can be a precise, minimally invasive treatment method for liver cancer and liver metastases. However, the effectiveness of SBRT relies on the accurate delivery of the dose to the tumor while sparing healthy tissue. Challenges persist in ensuring breath-hold reproducibility, with current methods often requiring manual verification of liver dome positions from kV-triggered images. To address this, we propose a proof-of-principle study of a deep learning-based pipeline to automatically delineate the liver dome from kV-planar images. From 24 patients who received SBRT for liver cancer or metastasis inside liver, 711 KV-triggered images acquired for online breath-hold verification were included in the current study. We developed a pipeline comprising a trained U-Net for automatic liver dome region segmentation from the triggered images followed by extraction of the liver dome via thresholding, edge detection, and morphological operations. The performance and generalizability of the pipeline was evaluated using 2-fold cross validation. The training of the U-Net model for liver region segmentation took under 30 minutes and the automatic delineation of a liver dome for any triggered image took less than one second. The RMSE and rate of detection for Fold1 with 366 images was (6.4 +/- 1.6) mm and 91.7%, respectively. For Fold2 with 345 images, the RMSE and rate of detection was (7.7 +/- 2.3) mm and 76.3% respectively.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 38 canonical work pages

  1. [1]

    Online verification of breath- hold reproducibility using kV-triggered imaging for liver stereotactic body radiation therapy

    Guo B, Stephans K, Woody N, Antolak A, Moazzezi M, Xia P. Online verification of breath- hold reproducibility using kV-triggered imaging for liver stereotactic body radiation therapy. J Appl Clin Med Phys. 2023;24(9):e14045. doi:10.1002/acm2.14045

    work page doi:10.1002/acm2.14045 2023

  2. [2]

    Stereotactic body radiation therapy in patients with hepatocellular carcinoma: A mini-review

    Gerum S, Jensen AD, Roeder F. Stereotactic body radiation therapy in patients with hepatocellular carcinoma: A mini-review. World J Gastrointest Oncol. 2019;11(5):367-376. doi:10.4251/wjgo.v11.i5.367 23

    work page doi:10.4251/wjgo.v11.i5.367 2019

  3. [3]

    The use of active breathing control (ABC) to reduce margin for breathing motion

    Wong JW, Sharpe MB, Jaffray DA, et al. The use of active breathing control (ABC) to reduce margin for breathing motion. Int J Radiat Oncol Biol Phys. 1999;44(4):911-919. doi:10.1016/s0360-3016(99)00056-5

    work page doi:10.1016/s0360-3016(99)00056-5 1999

  4. [4]

    Reproducibility of liver position using active breathing coordinator for liver cancer radiotherapy

    Eccles C, Brock KK, Bissonnette JP, Hawkins M, Dawson LA. Reproducibility of liver position using active breathing coordinator for liver cancer radiotherapy. Int J Radiat Oncol Biol Phys. 2006;64(3):751-759. doi:10.1016/j.ijrobp.2005.05.066

    work page doi:10.1016/j.ijrobp.2005.05.066 2006

  5. [5]

    Dosimetric assessment of patient- specific breath-hold reproducibility on liver motion for SBRT planning

    Lu L, Ouyang Z, Lin S, Mastroianni A, Stephans KL, Xia P. Dosimetric assessment of patient- specific breath-hold reproducibility on liver motion for SBRT planning. J Appl Clin Med Phys. 2020;21(7):77-83. doi:10.1002/acm2.12887

    work page doi:10.1002/acm2.12887 2020

  6. [6]

    The reproducibility of organ position using active breathing control (ABC) during liver radiotherapy

    Dawson LA, Brock KK, Kazanjian S, et al. The reproducibility of organ position using active breathing control (ABC) during liver radiotherapy. Int J Radiat Oncol Biol Phys. 2001;51(5):1410-1421. doi:10.1016/s0360-3016(01)02653-0

    work page doi:10.1016/s0360-3016(01)02653-0 2001

  7. [7]

    Accuracy of daily image guidance for hypofractionated liver radiotherapy with active breathing control

    Dawson LA, Eccles C, Bissonnette JP, Brock KK. Accuracy of daily image guidance for hypofractionated liver radiotherapy with active breathing control. Int J Radiat Oncol Biol Phys. 2005;62(4):1247-1252. doi:10.1016/j.ijrobp.2005.03.072

    work page doi:10.1016/j.ijrobp.2005.03.072 2005

  8. [8]

    Hypofraction radiotherapy of liver tumor using cone beam computed tomography guidance combined with active breath control by long breath-holding

    Zhong R, Wang J, Jiang X, et al. Hypofraction radiotherapy of liver tumor using cone beam computed tomography guidance combined with active breath control by long breath-holding. Radiother Oncol J Eur Soc Ther Radiol Oncol. 2012;104(3):379-385. doi:10.1016/j.radonc.2011.11.007

    work page doi:10.1016/j.radonc.2011.11.007 2012

Show all 42 references
  1. [9]

    Assessment of residual error in liver position using kV cone-beam computed tomography for liver cancer high-precision radiation therapy

    Hawkins MA, Brock KK, Eccles C, Moseley D, Jaffray D, Dawson LA. Assessment of residual error in liver position using kV cone-beam computed tomography for liver cancer high-precision radiation therapy. Int J Radiat Oncol Biol Phys. 2006;66(2):610-619. doi:10.1016/j.ijrobp.2006.03.026

    2006 doi

  2. [10]

    Interfractional diaphragm changes during breath-holding in stereotactic body radiotherapy for liver cancer

    Kawahara D, Ozawa S, Nakashima T, et al. Interfractional diaphragm changes during breath-holding in stereotactic body radiotherapy for liver cancer. Rep Pract Oncol Radiother J Gt Cancer Cent Poznan Pol Soc Radiat Oncol. 2018;23(2):84-90. doi:10.1016/j.rpor.2018.01.007

    2018 doi

  3. [11]

    Availability of applying diaphragm matching with the breath-holding technique in stereotactic body radiation therapy for liver tumors

    Kawahara D, Ozawa S, Kimura T, et al. Availability of applying diaphragm matching with the breath-holding technique in stereotactic body radiation therapy for liver tumors. Phys Medica PM Int J Devoted Appl Phys Med Biol Off J Ital Assoc Biomed Phys AIFB. 2016;32(4):557-561. d...

    2016 doi

  4. [12]

    Feasibility of Optical Surface-Guidance for Position Verification and Monitoring of Stereotactic Body Radiotherapy in Deep-Inspiration Breath-Hold

    Naumann P, Batista V , Farnia B, et al. Feasibility of Optical Surface-Guidance for Position Verification and Monitoring of Stereotactic Body Radiotherapy in Deep-Inspiration Breath-Hold. Front Oncol. 2020;10:573279. doi:10.3389/fonc.2020.573279

    2020

  5. [13]

    Active breathing control in combination with ultrasound imaging: a feasibility study of image guidance in stereotactic body radiation therapy of liver lesions

    Bloemen-van Gurp E, van der Meer S, Hendry J, et al. Active breathing control in combination with ultrasound imaging: a feasibility study of image guidance in stereotactic body radiation therapy of liver lesions. Int J Radiat Oncol Biol Phys. 2013;85(4):1096-1102. doi:10.1016/...

    2013 doi

  6. [14]

    Magnetic Resonance-Guided Stereotactic Body Radiotherapy of Liver Tumors: Initial Clinical Experience and Patient-Reported Outcomes

    Weykamp F, Hoegen P, Klüter S, et al. Magnetic Resonance-Guided Stereotactic Body Radiotherapy of Liver Tumors: Initial Clinical Experience and Patient-Reported Outcomes. Front Oncol. 2021;11. Accessed December 12, 2023. https://www.frontiersin.org/articles/10.3389/fonc.2021.610637

    2021

  7. [15]

    Real-time Magnetic Resonance-guided Liver Stereotactic Body Radiation Therapy: An Institutional Report Using a Magnetic Resonance-Linac System

    Feldman AM, Modh A, Glide-Hurst C, Chetty IJ, Movsas B. Real-time Magnetic Resonance-guided Liver Stereotactic Body Radiation Therapy: An Institutional Report Using a Magnetic Resonance-Linac System. Cureus. 11(9):e5774. doi:10.7759/cureus.5774

    doi

  8. [16]

    Automated estimation of the upper surface of the diaphragm in 3-D CT images

    Zhou X, Ninomiya H, Hara T, et al. Automated estimation of the upper surface of the diaphragm in 3-D CT images. IEEE Trans Biomed Eng. 2008;55(1):351-353. doi:10.1109/TBME.2007.899337

    2008

  9. [17]

    Diaphragm dome surface segmentation in CT data sets: a 3D active appearance model approach

    Beichel R, Gotschuli G, Sorantin E, Leberl FW, Sonka M. Diaphragm dome surface segmentation in CT data sets: a 3D active appearance model approach. In: Medical Imaging 2002: Image Processing. V ol 4684. SPIE; 2002:475-484. doi:10.1117/12.467190

    2002 doi

  10. [18]

    Automatic delineation of the diaphragm in computed tomographic images

    Rangayyan RM, Vu RH, Boag GS. Automatic delineation of the diaphragm in computed tomographic images. J Digit Imaging. 2008;21 Suppl 1(Suppl 1):S134-147. doi:10.1007/s10278-007-9091-y

    2008 doi

  11. [19]

    Anatomy-based algorithm for automatic segmentation of human diaphragm in noncontrast computed tomography images

    Karami E, Wang Y , Gaede S, Lee TY , Samani A. Anatomy-based algorithm for automatic segmentation of human diaphragm in noncontrast computed tomography images. J Med Imaging. 2016;3(4):046004. doi:10.1117/1.JMI.3.4.046004

    2016 doi

  12. [20]

    Accessed November 10, 2023

    ImageNet. Accessed November 10, 2023. https://www.image-net.org/

    2023

  13. [21]

    A Review of Deep Transfer Learning and Recent Advancements

    Iman M, Arabnia HR, Rasheed K. A Review of Deep Transfer Learning and Recent Advancements. Technologies. 2023;11(2):40. doi:10.3390/technologies11020040

    2023 doi

  14. [22]

    Holistically-Nested Edge Detection

    Xie S, Tu Z. Holistically-Nested Edge Detection. Published online October 3, 2015. Accessed February 15, 2023. http://arxiv.org/abs/1504.06375

    2015 arXiv

  15. [23]

    Accessed November 10, 2023

    TensorFlow. Accessed November 10, 2023. https://www.tensorflow.org/

    2023

  16. [24]

    Accessed November 10, 2023

    OpenCV: Histograms - 2: Histogram Equalization. Accessed November 10, 2023. https://docs.opencv.org/4.x/d5/daf/tutorial_py_histogram_equalization.html

    2023

  17. [25]

    Adaptive Histogram Equalization and Its Variations

    Pizer SM, Amburn EP, Austin JD, et al. Adaptive Histogram Equalization and Its Variations

  18. [26]

    ashukid/hed-edge-detector

    Chandra A. ashukid/hed-edge-detector. Published online October 25, 2023. Accessed November 10, 2023. https://github.com/ashukid/hed-edge-detector

    2023

  19. [27]

    Rectified Linear Units Improve Restricted Boltzmann Machines

    Nair V , Hinton GE. Rectified Linear Units Improve Restricted Boltzmann Machines

  20. [28]

    Accessed November 10, 2023

    Albumentations. Accessed November 10, 2023. https://albumentations.ai/ 25

    2023

  21. [29]

    An overview of gradient descent optimization algorithms

    Ruder S. An overview of gradient descent optimization algorithms. Published online June 15, 2017. Accessed January 8, 2024. http://arxiv.org/abs/1609.04747

    2017 arXiv

  22. [30]

    grand-challenge.org

    FLARE21 - Grand Challenge. grand-challenge.org. Accessed November 10, 2023. https://flare.grand-challenge.org/

    2023

  23. [31]

    grand-challenge.org

    CHAOS - Grand Challenge. grand-challenge.org. Accessed November 10, 2023. https://chaos.grand-challenge.org/

    2023

  24. [32]

    Accessed November 10, 2023

    The 2023 Kidney Tumor Segmentation Challenge. Accessed November 10, 2023. https://kits-challenge.org/kits23/

    2023

  25. [33]

    U-Net and its variants for medical image segmentation: theory and applications

    Siddique N, Sidike P, Elkin C, Devabhaktuni V . U-Net and its variants for medical image segmentation: theory and applications

  26. [34]

    U-Net: Convolutional Networks for Biomedical Image Segmentation

    Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science. Springer Internat...

    2015 doi

  27. [35]

    UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation

    Pravitasari AA, Iriawan N, Almuhayar M, et al. UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA Telecommun Comput Electron Control. 2020;18(3):1310. doi:10.12928/telkomnika.v18i3.14753

    2020 doi

  28. [36]

    Improved U-Net architecture with VGG-16 for brain tumor segmentation

    Ghosh S, Chaki A, Santosh K. Improved U-Net architecture with VGG-16 for brain tumor segmentation. Phys Eng Sci Med. 2021;44(3):703-712. doi:10.1007/s13246-021-01019- w

    2021 doi

  29. [37]

    Building segmentation from satellite imagery using U-Net with ResNet encoder

    Liu Z, Chen B, Zhang A. Building segmentation from satellite imagery using U-Net with ResNet encoder. In: 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). ; 2020:1967-1971. doi:10.1109/ICMCCE51767.2020.00431

    2020

  30. [38]

    Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

    Abedalla A, Abdullah M, Al-Ayyoub M, Benkhelifa E. Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures. PeerJ Comput Sci. 2021;7:e607. doi:10.7717/peerj-cs.607

    2021 doi

  31. [39]

    Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

    Rampun A, López-Linares K, Morrow PJ, et al. Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network. Med Image Anal. 2019;57:1-17. doi:10.1016/j.media.2019.06.007

    2019 doi

  32. [40]

    Early Breast Cancer Screening from Thermography via Deep Pre-Trained Edge Detection with Extreme Gradient Boosting

    Gamal S, Atef H, Youssef D, Ismail T, El-Azab J. Early Breast Cancer Screening from Thermography via Deep Pre-Trained Edge Detection with Extreme Gradient Boosting. In: 2023 Intelligent Methods, Systems, and Applications (IMSA). ; 2023:430-433. doi:10.1109/IMSA58542.2023.10217569

    2023

  33. [41]

    Gastro-Intestinal Tract Image Segmentation using Edge U-Net and U-Net VGG19

    Talakola S, Reddy MS, Nagam R, Chebrolu S. Gastro-Intestinal Tract Image Segmentation using Edge U-Net and U-Net VGG19. In: 2023 International Conference on 26 Computer, Electronics & Electrical Engineering & Their Applications (IC2E3). ; 2023:1-7. doi:10.1109/IC2E357697.2023.10262483

    2023

  34. [42]

    Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation

    Roth HR, Lu L, Lay N, et al. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med Image Anal. 2018;45:94-107. doi:10.1016/j.media.2018.01.006

    2018 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.