Generative adversarial learning for semi-supervised retinal layer segmentation in OCT images

Authors: charlene  zhi
lin  ong
asad  abu
bakar  ali
jagath c rajapakse
Venue: IEEE_org_EMBS_BHI_2024_Conference
Published: 2024-06-13 05:52:06

Abstract: It is often challenging to obtain large number of labeled data for retinal layer segmentation in optical coherence tomography scans due to the need for expert ophthalmologists. On the other hand, huge quantities of unlabeled scans are often collected in medical centers. In this work, we propose a novel generative adversarial learning framework, GOctSeg, for semi- supervised retinal layer segmentation. GOctSeg consists of a U- Net generator, a discriminator, and a segmentor. The generation of synthetic images from synthetic labels is performed with the U-Net generator, which is used as input together with labeled B-scan patches into the Segmentor network for retinal layer boundary regression. We have also evaluated our methodology on two datasets, the Data Resource for Multiple Sclerosis and Healthy Controls and the Duke University Diabetic Macular Edema dataset and demonstrated that our method outperformed or is comparable to other state-of-the-art methods with limited labels for boundary regression. Furthermore, we investigated the performance of GOctSeg on images with low signal-to-noise ratio or with blurred boundaries and showed that our methodology remained robust. Through ablation studies, we demonstrated the utility of synthetic labels for generative learning to guide in semi-supervised retinal layer segmentation. We envision that this methodology can be used to significantly reduce the effort required to obtain labels when there is label scarcity in clinical settings.

Main paper: 
 i. introduction: 
 Retinal layer thickness is a critical imaging biomarker for both ophthalmic and nonophthalmic diseases. Retinal layer thickness can be measured noninvasively at micrometerresolution by optical coherence tomography (OCT) [1] . The OCT scans are usually acquired in 3D volumes of B-scans, with each B-scan as a sequence of A-scans. Each A-scan, or single axial scans, indicates the composition of optical reflectance at a certain point through the tissue depth. The sequence of A-scans makes up a cross-sectional image of the retina, or B-scan, by imaging across the tissue [2] . Retinal layer thickness has been shown to be a useful diagnostic biomarker for early age-related macular degeneration, where studies have shown a statistically significant thickening of retinal pigment epithelium and a reduction of photoreceptor thickness [3] . In addition, it also has promising applications in early diagnosis of Alzheimer's Disease (AD) where significant thinning of retinal nerve fiber layer and inner and outer macular rings were observed in AD subjects [4] . OCT is commonly used to image the retinal layers, which are layers of multiple structured layers of cells and synapses. These layers are shown in Fig. 1 . However, manual ground truth of retinal layers is often challenging to obtain, and associated with potential inter and intra-observer variability. Moreover, speckle noise and blurring due to motion are typically present in OCT images [5] , [6] . To reduce the need for manual labels, automatic OCT layer segmentation algorithms have been developed over the years.Recently, deep learning methods have been proposed to be used for retinal layer segmentation and achieved state-ofthe-art results. A number of these approaches are based on either convolutional neural networks [7] , [8] or long short-term memory networks (LSTM) [9] , which require large labeled datasets for training. However, label paucity due to the need for expert ophthalmologists input necessitates the need for semi-supervised approaches to reduce the annotation burden required. To reduce annotation need, multiple approaches, such as pseudo-labelling [10] , vector quantization [11] , and task and data level consistency supervision [12] were introduced. Clustering approaches [13] were also introduced in other areas such as intravascular OCT. In addition, as each retinal layer is extremely thin, sub-pixel accuracy is desired for retinal layer segmentation. Another feature of these retinal layers is that they have strict biological ordering. To address the need for sub-pixel level accuracy and anatomical constraints, He et al. [14] proposed a residual U-Net with two output branches to obtain pixel-wise layer segmentation probability prediction and a softmax mapping for probability distribution of boundary predictions. Topological ordering was imposed using an iterative topology module. SD-LayerNet [15] , by Fazekas et al., also ensures the correct topological ordering through the use of a fully differentiable topological engine. Adversarial learning has also been used for semi-supervised segmentation. SGNet consists of a segmentation and a discriminator network to utilize unlabeled data [16] .In this paper, we propose a novel generative adversarial learning for semi-supervised retinal layer segmentation, called GOctSeg. GOctSeg consists of a Generator, a Discriminator, and a Segmentor network, which is trained in an end-to-end manner. We hypothesize that by utilizing prior knowledge of the biological ordering of retinal layers to generate synthetic labels, and the adversarial learning of the distribution of retinal images, GOctSeg requires less labeled data to attain reasonable performance for layer segmentation. The contributions of the paper are summarized as follows:1) We propose a generative adversarial learning approach for semi-supervised retinal layer segmentation. 2) We propose to synthesize labels and incorporate the knowledge of the biological ordering of retinal layers for model training. 3) Instead of utilizing all the labels, we use a limited number of labels, namely, 6, 30, and 60 labels, for model training. 4) We investigated the performance of our approach on noisy and diseased images and demonstrated that our approach was able to perform consistently even in images with low signal-to-noise ratio or with blurred boundaries.The proposed model (Fig. 2 ), named as GOctSeg, consists of a Generator G, a Segmentor S, and a Discriminator D. G generates OCT images I s from an input synthetic layer label Y s , where s indicates that it is synthetic. D aims to differentiate the synthetic OCT images I s from real OCT images I r , where r indicates a real OCT image. S aims to segment the layers of I s , which is expected to be the same as Y s . Simultaneously, it aims to segment the layers of labeled OCT images I l .G has a U-Net structure which consists of 4 blocks in the contracting path and 3 blocks in the expansive path. Each block consists of 2 convolutional layers with group normalization, ReLU, and dropout of 0.5 between them. The use of dropouts was added during experimentation to improve the model performance. The output layer is a 1 × 1 convolutional layer with 1 output channel.D tries to minimize the distribution differences between I s and I r through their encodings D(I s ) and D(I r ). This allows the synthetically generated image I s to be as similar as possible to an actual OCT image I r in terms of the appearance and texture. It consists of 5 convolutional layers, with 64, 128, 256, 512, and 1 output channels, respectively. The architecture is adapted from [17] , with batch-normalization and LeakyReLU, except that there is no batch normalization between the first and second convolutional layer, and the last layer.S maps the synthetically generated image I s to predicted labels Ŷs and Bs , or image I l to Ŷl and Bl . In our implementation, each batch contains data from synthetic images I s and real-world labeled OCT images I l in a 1:1 ratio. The architecture of the Segmentor is given in Fig. 3 . It comprises an Attention U-Net of 7 convolutional blocks, followed by two output branches. The first branch, conv-s, outputs a pixelwise segmentation of the different layers, whereas the second branch, conv-b, outputs N boundary position distributions. Both branches, conv-s and conv-b, consist of a residual block followed by a 1 × 1 convolutional layer, similar to that in [14] . Each convolutional block consists of two 3 × 3 convolutional layers with group normalization, ReLU activation, and for the contracting path, a dropout of 0.5. In the expansive path, an attention gate module [18] is introduced.1) Gated Attention: Let l denote a convolution layer in the last 3 convolutional blocks (l = 5, 6, 7). Gated attention [18] utilizes the skip connection feature input q 8-l for 5 ≤ l ≤ 7 and the output from the previous block o l-1 to generate an attention map α l . o l-1 acts as a query to determine focus areas of q 8-l . The output o l-1 consists of N F,l-1 feature maps, where F represents feature maps. A 1 × 1 convolution and group normalization are performed on o l-1 to obtain z l :EQUATIONwhere W l o ∈ R N F,l ×N F,l-1 ×1×1 and β l o denote the weight matrix and bias vector for the layer l. This is followed by an upsampling operation to obtain ẑl . Similarly, a 1 × 1 convolution and group normalization are performed on skip connection feature input, q 8-l , to obtain output q8-l :EQUATIONW 8-l q ∈ R N F,l ×N F,8-l ×1×1 and β 8-l q denote the skipped connection weights and biases, respectively.The attention scores α l are computed for convolutional block l asα l = ρ 1 BatchNorm W l α (max(0, (ẑ l + q8-l ))) + β l α .(3) where W l α ∈ R 1×N F,l ×1×1 are convolution weights with 1 × 1 kernel size. ρ 1 refers to the nonlinear sigmoid operation. The attention scores are then multiplied elementwise with the skip connection feature input, q 8-l , to identify salient features in the skip connection. The attention scores weigh the importance of the different parameters in q 8-l to the final output. q 8-l α = q 8-l * α l (4) * denotes element-wise multiplication. o l-1 is upsampled from the previous layer using bilinear interpolation to obtain ôl-1 . Finally, q 8-l α is concatenated with the upsampled output ôl-1 . In conv-b, the output from the Attention U-Net is passed into a residual block followed by a 1 × 1 convolutional layer with N channels. A column-wise softmax is performed to obtain boundary position probabilities p( B; θ) for input image I. The final boundary position B is obtained with soft argmax across J rows as in [14] :B = J j=1 j • p( B|I; θ).For real OCT images I l , topological correction is imposed on {B x } x=X x=1 for all X A-scans to consider the anatomical constraint of the retinal layers [14] , [15] .EQUATIONwhere n denotes the n th layer of the total N layers.Two independent datasets were used.The DRMSHC [14] , [19] , [20] 1 consists of OCT volumetric scans of 14 healthy and 21 multiple sclerosis (MS) patients acquired using a Spectralis OCT system. Each macular cube scan consists of 49 B-scans, where 1 B-scan consists of 1024 A-scans and each A-scan has 496 pixels with a resolution of approximately 3.87 micrometers per pixel. Manual annotations delineate 8 layers, namely, Retinal Nerve Fiber Layer (RNFL), Ganglion Cell Layer (GCL) + Inner Plexiform Layer (IPL), Inner Nuclear Layer (INL), Outer Plexiform Layer (OPL), Outer Nuclear Layer (ONL), Inner Photoreceptor Segments (IS), Outer Photoreceptor Segments (OS), and Retinal Pigment Epithelium (RPE) resulting in 9 boundaries. The dataset was split into 12, 3, 20 patients for training, validation, and testing. The first 8 and 12 participants who are healthy and have multiple sclerosis respectively were selected for the test dataset, totaling 980 B-scans.2) Duke DME: The Duke University DME dataset (Duke DME) [21] consists of 10 volumetric scans of patients with severe DME and acquired with the Spectralis device with an axial resolution of 3.9 micrometers per pixel and a lateral resolution of 11.07 to 11.59 micrometers per pixel. Each volumetric scan consists of 61 B-scans, of which 11 B-scans from 10 patients are annotated by 2 ophthalmologists and the rest are unlabeled. There are 7 layers delineated: RNFL, GCL + IPL, INL, OPL, ONL + IS, OS, and RPE. In addition, there are unannotated regions in certain A-scans for certain layers. 3-fold cross validation was used.During training, each B-scan was cropped into images of 496 × 128 pixels with a stride of 64. For DRMSHC, this corresponded to 15 images, resulting in 8820 and 2205 images in the training and validation dataset, respectively. The whole DRMSHC training dataset was used as the I r . For the Duke DME dataset, A-scans corresponding to regions where annotations were missing in the periphery of the labeled Bscans were removed. In total, there were 6 to 7 images per Bscan for the labeled dataset, corresponding to 748 images, and 11 images per B-scan for the unlabeled dataset, corresponding to 5500 images. The labeled B-scans were used as I l and both labeled and unlabeled B-scans were used as I r .Each B-scan, their ground truth label, and boundaries were flattened to the estimate of the Bruch's membrane (BM) and their background was removed [22] . The preprocessing steps were based on https://github.com/heyufan1995/oct preprocess. They were shifted to a mean starting position of 248 pixels (half the height of the B-scan) and a standard deviation of 30 pixels, modelling on a normal distribution to add variability in vertical position. Training with full-width OCT B-scans was limited by the available RAM and cropping increased data size. At test time, the B-scan was not cropped to save time, but was flattened as a whole [23] . It was shifted vertically by 248 pixels, and the background was removed.Synthetic labels Y s and corresponding boundaries B s were generated during data preparation. As a simplification, the 1 It is obtained from https://iacl.ece.jhu.edu/index.php/Resources thickness of the nth retinal layer was modelled as a random normal variable t n with mean µ tn and standard deviation σ tn . During training, average thickness µ tn and the standard deviation σ tn of the layer thickness were computed for the training dataset. Through computing the thickness of all layers and applying random vertical translations, we obtained Y s . B s was generated by computing the vertical positions at each A-scan of the layer surfaces. The synthetic labels incorporated prior information regarding the positional ordering and thickness of the different retinal layers.The model was trained in three steps. Firstly, the Discriminator was updated via the discriminator losses. Next, we computed the semi-supervised Segmentor losses J l1,s and J dice,s and the Generator loss on the synthetic images. As the synthetic images from the Generator were used as inputs into the Segmentor, J l1,s and J dice,s were used to train the Generator as well via backpropagation. Finally, we finetuned the Segmentor with J l1,l and J dice,l on the labeled images.1) Generator Loss: The generator loss minimizes the distribution differences between the generated synthetic images and real OCT images:EQUATION)where r represents whether the image is real, and is an array of 1.2) Segmentor Loss: Assuming that I s falls within the distribution of I l , the segmentation of I s to obtain predicted Ŷs should be as similar as possible to the original synthetic labels Y s . Similarly, the segmentation of I l to obtain predicted Ŷl and should also be as similar as possible to the ground truth labels Y l . We compute the segmentor losses as a combined Dice and Smooth ℓ 1 loss.The Dice loss can be computed between predicted Ŷs and original synthetic labels Y s , and between Ŷl and Y l , with ϵ as a smoothing constant. Let v(j, x) be a pixel of j-th row and x-th A-scan, and V be the set of all pixels. The Dice loss between Ŷs and Y s is given as follows:J dice,s = 1 N s + 1 Ns+1 s=1 (1- ϵ + v∈V 2 Ŷs (v)Y s (v) ϵ + v∈V Y s (v) + v∈V Ŷs (v)).(7) The smooth ℓ 1 loss can be used between Bs and the synthetic ground truth boundaries B s , and between Bl and labeled ground truth boundaries B l [14] . The smooth ℓ 1 loss between Bs and B s is given as follows:J l1,s = 1 N b X N b b=1 Nx x=1 (0.5(d x ) 2 1(|d x | < 1) + (|d x | -0.5)1(|d x | ≥ 1)) (8)where d x represents the difference between the predicted and the actual boundary positions for 1 A-scan.The combined loss for the joint training of the generator and segmentor using synthetic images is computed byJ s = λ 1 J dice,s + λ 2 J ℓ1,s + J G . (9)For the labeled images, it is computed byEQUATIONwhere J dice and J ℓ1 denote the Dice loss and smooth ℓ 1 loss respectively and λ 1 , λ 2 , λ 3 control the balance of the losses. λ 1 , λ 2 , λ 3 are weighing hyperparameters to be optimized.3) Discriminator Loss: The loss function for the Discriminator is computed asEQUATION)where r and s represent whether the image is real or synthetic via arrays of 1 or 0.The models were trained on a NVIDIA Tesla V100 GPU, using Python 3.7.12 and PyTorch 1.11.0. Adam optimizer with a beta of 0.5 was used.1) DRMSHC: Hyperparameter optimization was performed with T une library [24] using the training partition with 6 randomly selected labeled images as I l . Random search of 15 trials with Async Successive Halving Algorithm (ASHA) scheduler was performed over the parameter space of learning rate λ lr = [1×10 -5 , 5×10 -4 , 1×10 -4 , 5×10 -3 , 1×10 -3 , 5× 10 -2 , 1×10 -2 ] and weighting parameters, λ 1 to λ 3 which was [0.02, 0.1, 0.5, 1, 5, 10, 50] for 50 epochs. The hyperparameters and epoch that yielded the lowest mean absolute error (MAE) on the validation set were selected, which were: λ 1 = 0.5, λ 2 = 0.5, λ 3 = 0.02, λ lr = 0.001, and epoch as 25. Next, we combined the validation and training set to train the model.2) Duke DME: Nested 3-fold cross validation was performed, with the outer loop used to estimate the model generalization error and the inner loop used for hyperparameter optimization. In each inner loop, 15 random trials were performed over the same parameter space of λ lr , λ 1 , λ 2 , and λ 3 as DRMSHC and the hyperparameters which yielded the lowest MAE on the validation fold were selected. As only 11 of 61 B-scans from each volumetric scan were labeled, both the labeled and unlabeled images were used to train the Discriminator. During training, A-scans with missing annotation were removed from the loss computation. After tuning, we combined the validation and training partition of inner loop to obtain the outer train set for each outer fold.The images were augmented by random horizontal flipping with a probability of 0.5. Batch size of 8, with 4 labeled and 4 unlabeled images, was used. Instead of training the model using all labels, we selected the following quantities for training, with the rest of images used as I r : (i) 6 labeled images; or (ii) 30 labeled images; or (iii) 60 labeled images, randomly selected from the whole labeled dataset.At test time, only the Segmentor network was used to predict on the test dataset.We implemented our approach on the DRMSHC dataset and compared with SD-LayerNet [15] 2 , a semi-supervised approach; the method by He et al. [14] , which is fully supervised, and which we termed as Structured-Layer; and SGNet, another semi-supervised approach [16] . For comparison, we performed similar preprocessing steps involving retina flattening, background removal, and patch extraction for all methods. SD-LayerNet was implemented with 1 texture factor, batch size of 8 with 4 labeled and 4 unlabeled images, and gradient clipping of 0.5. To standardize for time and computational resources, we standardized the epochs for both SD-LayerNet and GOctSeg. For Structured-Layer, it was replicated based on [14] , except with no vertical scaling, with gradient clipping of 0.5, same weight for pixels around the surfaces, and batch size of 4. Hyperparameter optimization determined the optimal epoch for convergence. For SGNet, the implementation was replicated based on [16] , with lambda as 1, no random cropping, and gradient clipping of 20. The median frequency balance was computed based on the labeled data. Hyperparameter optimization determined the optimal learning rate and epoch to prevent instabilities. To obtain the MAE and root-mean-square error (RMSE) for SGNet, which is a segmentation approach, boundary tracing was done on the predictions. 3-fold cross validation was performed for all methods for Duke DME dataset. We evaluated all methods on the test dataset on MAE, RMSE, and Dice.From Table I , it can be seen that our approach was statistically significantly better than other approaches for 6, 30 and 60 images using the one-tailed Wilcoxon signed-rank test for MAE and RMSE (p-values of comparison for 6 images for MAE: GOctSeg vs SD-LayerNet: 3.6 × 10 -162 , GOctSeg vs SGNet: 4.8 × 10 -162 , GOctSeg vs Structured-Layer: 3.0×10 -162 ). Using Dice, we observed that Structured-Layer performed better for 30 and 60 images, as semantic segmentation is an easier task than boundary regression. When the experiment for Structured-Layer was repeated with the full labeled dataset, we obtained 3.78µm for MAE, which is almost equivalent to the MAE of GOctSeg for 60 labeled images.When we computed the MAE by the different boundaries as seen in Fig. 4 , our approach performed comparably across all boundaries, other than the boundary between RNFL and GCL+IPL. In contrast, Structured Layer trained on 6 labeled images had very large mean MAE for many boundaries. Although SD-LayerNet also performed reasonably well, there was a higher mean MAE for all boundaries with 6 labeled images, for all boundaries except upper boundary of RNFL for 30 images, and for all boundaries with 60 labeled images. While GOctSeg should perform better with 30 labeled images, the slightly higher MAE was due to outliers in the predicted segmentation for a minority of the images. When comparing the 50th percentile of mean MAE of B-scan, we obtained 3.85µm with 6 images, compared to 3.74µm with 30 images.We also conducted two ablation studies, (i) training the Segmentor network in a fully supervised manner (GOctSeg-Sup) and (ii) training the Generator and Disciminator first to generate synthetic images, followed by training the Segmentor on the synthetic and labeled images (GOctSeg-Sep). Table II shows the model performance of GOctSeg, GOctSeg-Sup, and GOctSeg-Sep. We observed that our approach had the lowest mean MAE and RMSE in the 6 and 60 images setting (p-values of MAE for 6 images' comparison: GOctSeg vs GOctSeg-Sup: 3.0 × 10 -162 , GOctSeg vs GOctSeg-Sep: 2.3 × 10 -137 ). Using one-sided Wilcoxon signed-rank test, we obtained statistical significance of p = 1.1 × 10 -6 when comparing GOctSeg-Sep and GOctSeg for 30 labeled images, with GOctSeg having more predictions with a lower MAE. Upon investigation, the 50th percentile of mean MAE per bscan for GOctSeg-Sep with 30 labeled images was 3.81µm compared to 3.74µm for GOctSeg.The Duke DME dataset contains eyes with severe DME pathology. Table III shows a comparison of the mean metric and range of GOctSeg, SD-LayerNet, SGNet, and Structured-Layer on the Duke DME dataset for 6, 30, and 60 labeled images for 3-fold cross validation. Our approach performed significantly better than SD-LayerNet, SGNet, and Structured Layer for 6 labeled images on MAE and RMSE, as seen from Table III . From Fig. 5 we observed that GOctSeg had smoother segmentation compared to SD-LayerNet at 6 images. For the limited number of labeled images, Structured Layer appeared to be unable to delineate the layers properly, but was able to do so with enough labeled images, i.e. 60 images.Next, we investigated the performance of GOctSeg with SD-LayerNet under different noisy scenarios at 6 images. We compared with SD-LayerNet as it had the closest performance to GOctSeg. In the first three scenarios, we investigated reducing the signal-to-noise ratio of the images by approximately 10%, 20%, and 40% (10% red, 20% red, and 40% red on Table IV ). This was done by adding Gaussian noise with mean of 0 and standard deviation, empirically determined by calculating the decrease in signal-to-noise ratio via adding varying amounts of Gaussian noise, to the volumetric scans and then clipping the images from 0 to 1. In the next two scenarios, we investigated adding Gaussian noise only at the high frequency components for boundary blurring. In the fourth scenario (i.e. FT on Table IV ), we performed Fourier transform on the image and adding Gaussian noise at the periphery of the real component of the b Dice computed from predicted boundaries. GO: GOctSeg, GO-Sup: GOctSeg-Sup, GO-Sep: GOctSeg-Sep. complex image. We performed inverse Fourier transform to get the new images. In the fifth scenario (i.e. HPF on Table IV ), we performed high pass filtering of the image, added Gaussian noise of mean 0 and sigma 0.05 to the high pass filtered image, which was added with the low pass filtered image.As shown on Table IV which compares the models over various noisy conditions, GOctSeg performed robustly in most noisy conditions and performed better in 20% SNR reduction, 10% SNR reduction, and boundary blurring conditions in MAE, RMSE and Dice compared to SD-LayerNet.In this paper, we introduced a semi-supervised learning generative adversarial learning framework for retinal layer segmentation. Via our methodology, we propose the generation of synthetic images from synthetic boundaries, which would be able to capture positional and intensity changes of the different layers. This allowed us to constraint the search space for mapping the image to the corresponding label, reducing the number of labels required for a good segmentation. As demonstrated above, when labels were limited, GOctSeg was able to perform almost on par to Structured-Layer trained on the full labeled dataset for DRMSHC. In addition, we demonstrated that GOctSeg was able to perform better than other approaches with 6 labeled images, in severe disease pathology cases. In such cases, the presence of pathologies such as intraretinal fluid would complicate the convergence of the algorithm due to unclear intensity changes in the retinal layers. Nonetheless, this did not significantly degrade the performance of GOctSeg. Furthermore, GOctSeg was robust in different noisy conditions. In particular, we compared the performance of GOctSeg with SD-LayerNet after reducing the signal-to-noise ratio and blurring boundary, and observed that it performed better compared to SD-LayerNet in most conditions. This is extremely promising in real-world scenarios, where OCT images usually have low signal-to-noise ratio [25] .Nevertheless, there are still limitations. Currently, preprocessing is required, to minimize the variability of the retina images for modeling the distribution with the Generator. The ground truth annotations provided might also have imperfections, although such slight imperfections do not affect the synthetic labels as the labels were generated based on the mean thickness and the standard deviations of the ground truth. One future improvement would be to generate more sophisticated labels which would capture curvature changes of the retina.In summary, GOctSeg would be extremely useful in obtaining a reasonable segmentation when labels are limited. Furthermore, given an estimate of the layer thickness, it is also possible to utilize the model to explore images in the absence of ground truth as the Segmentor could be trained on synthetically generated images. This could occur in situations where only the caliper measurements of the retina are available, which provides an estimate of the layer thickness but lacks the granularity offered by layer segmentation. In such scenarios, the model could be trained to generate synthetic images which could be used to learn the intensity and biological ordering of these layers. This would be critical in real world settings.The implementation of SD-LayerNet used was https://github.com/ABotond/SD-LayerNet. 
 
 Appendix:  



Reviews: review: title: title: A paper with solid results but may lack of novelty and generalizability
paper_summary: paper_summary: This paper proposed a semi-supervised learning framework for retinal layer segmentation. Relying upon the order of retinal layers, the authors used the conditional generative model to generate the synthetic retinal images based on the corresponding synthetic labels. Then, a segmentor is trained on the synthetic dataset and fine-tuned through the real-world data. The model is evaluated on two OCT datasets.
strength_weakness: strength_weakness: -- This study relies on a strong assumption that the retinal tissues have a clear, ordered layer structure that can be segmented based on intensity differences. The reviewer is unclear about its generalization capability on tissues with non-layered structures.  

-- The performance of the study ties heavily to the quality of the generated synthetic images. As such, is this study still validated on diseased models?

-- The idea of using the conditional generative model to get synthetic data to enrich the training set (data augmentation) is not very novel. 

-- B_s and B_y seem undefined. 

-- Some algorithm implementation details can be moved from the Methods section into the Experiments Section to improve the presentation of the paper.
questions: questions: See above weakness.
title: title: An effective semi-supervised learning approach for retina boundary segmentation combining both metrics the DICE factors and smoothness for evaluation
paper_summary: paper_summary: The authors present an improved semi-supervised learning approach for retinal boundary segmentation. The architecture consists of three main parts, a Generator, Segmentor, and Discriminator trained end-to-end. More specifically, the authors combine metrics describing the smoothness and DICE of the segmentation to adjust the training. In the experimental studies, the effectiveness of the approach is presented with significant improvements compared to other architectures. Here, the authors differentiate between the number of labelled images used for training and demonstrate the increasing performance for all the different boundary layers.
strength_weakness: strength_weakness: - A cross-validation study could strengthen the outcome of the presented results
- The discussion is a little short compared to the in part very detailed method sections
questions: questions: STOTA
- Many more studies are related to semi-supervised boundary segmentation, e.g., for IVOCT image data. It might be reasonable to extend your references. 

Methods:
- You mention that your learning approach is trained in an end-to-end fashion. Please emphasize more clearly: How are the different parts (Generator, Discriminator, and Segmentor) connected during training? In Fig. 2 it seems like the networks are trained separately.
- How did you derive the "prior information" for the synthetic labels? Please state a reference or describe your manual approach.
- Did you evaluate the performances with a cross-validation?
- Please explain the difference between the chosen hyperparameters for the DME and DRMHC datasets. In particular, \lambda_1 and \lambda_3 are chosen much higher for DME. Considering Eq. (9) and (10) the DICE coefficients are weighted higher - is there a specific reason to do so? Are the layers thicker/thinner in the DME dataset?

Results and Discussion:
- Use boundary names or abbreviations introduced in Fig. 1 for errorbar plots in Fig. 4? 
- Use the same y-axis scaling in Fig. 4? Or, at least, increase the number of ticks for the scenario with 6 labelled images.
- Ablation study: What data set sizes are used for the different approaches (-Sup, -Sep) in comparison to your best approach? 
- Improve the layout of results Tables.
title: title: My Review for IEEE EMBS BHI 2024 Submission 100
paper_summary: paper_summary: The paper uses GAN for the segmentation of the retinal layer in OCT images.
strength_weakness: strength_weakness: Why not use n-fold cross validation?
It seems difficult to understand that the MAE and RMSE results are significantly different (based on the very large standard deviations). Including the actual p-values might help the reader. Some more details on how Wilcoxon was used would be of interest.
questions: questions: See above.