A recurrent network for segmenting the thrombus on brain MRI in patients with hyper-acute ischemic stroke

Authors: sofia  vargas-ibarra
vincent  vigneron
hichem  maaref
jonathan  kobold
sonia  garcia-salicetti
nicolas  chausson
didier  smadja
Venue: MIDL_io_2024_Conference
Published: 2024-01-30 14:13:39

Abstract: In the stroke workflow, timely decision-making is crucial. Identifying, localizing, and measuring occlusive arterial thrombi during initial imaging is a critical step that triggers the choice of therapeutic treatment for optimizing vascular re-canalization. We present a recurrent model that segments the thrombus in patients suffering from a hyper-acute stroke. A cross-attention module is defined to merge the diffusion and susceptibility-weighted modalities available in Magnetic Resonance Imaging (MRI), which are fed to a modified version of the Convolutional Long-Short-Term Memory (CLSTM) model. It detects almost all the thrombi with a Dice higher than 0.6. The lesion segmentation prediction reduces the false positives to almost zero and the performance is comparable between distal and proximal occlusions.

Main paper: 
 introduction: 
 Stroke causes millions of deaths and cerebral palsy per day nowadays. Acute ischemic stroke (AIS) occurs when a blood clot (thrombus) blocks an artery, restricting average blood circulation and initiating brain tissue damage. The lesion caused by the stroke can be separated into (1) the penumbra, which represents the brain tissue that is potentially recoverable but temporarily injured, and (2) the core, the irreversibly damaged tissue. The lesion's size and location are crucial pieces of information that help the doctors locate the thrombi and decide the treatment. In an emergency case, the pathology is in the hyper-acute phase (<6h from the onset of the symptoms) which is critical for saving lives and improving life expectancy of patients.This paper proposes an experimentally validated and computationally affordable method capable of segmenting the thrombus of an AIS patient with few false positives using lesion segmentation as support. This technique is based on a light spatial recurrent neural network architecture.As the lesion is brighter (in the case of DWI) or darker (in ADC) depending on the MRI modalities, some approaches exploit these properties using non-learning algorithms. For instance, unsupervised clustering such as Fuzzy C-means produces considerably good automatic segmentation results (Tsai et al., 2014) . It is applied on intensity values lower than the histogram peak (healthy brain tissue) using a fixed threshold, and it eliminates the artifacts thanks to several modalities. Also, anomaly detection can be done on DWI as in (Nazari-Farsani et al., 2020) , where the Crawford-Howell t-test and the comparison of stroke images to healthy controls are used to develop a classifier to discriminate the images into stroke or non-stroke categories following the lesion segmentation. For the thrombi, (Santos et al., 2014) defined an automatic segmentation method in three steps: segmentation of the contralateral vasculature; creation of a mask in the occluded arterial segment by mapping the contralateral segmentation using mirror symmetry; and segmentation of the thrombus using intensity-based region growing. In (Löber et al., 2017) Random Forests are used to classify features extracted by thresholding and connected component clustering.ISLES challenges (Petzsche et al., 2022) provide several public datasets that allow the training of lesion segmentation models. To manage 3D inputs, 2D CNN models have been used (Clerigues et al., 2019 ), (Shah et al., 2020) , the mixture of 2D and 3D architectures have been proposed as D-Unet (Zhou et al., 2021) , DFENet (Basak et al., 2021) , and also using variations of Unet3D (Omarov et al., 2022) , (Ashtari et al., 2023) . Attention methods such as AABTS-Net (Tian et al., 2023) have been used, incorporating axial attention (Wang et al., 2020) , skip-connections as PerfUnet (de Vries et al., 2023) or soft-attention mechanisms as DRANet (Liu et al., 2020) . UNETR proposed by (Hatamizadeh et al., 2021) is an architecture adapted from Visual Transformers (Dosovitskiy et al., 2021) to 3D inputs for medical applications. But, all these methods rely on considerable big datasets and neither of them is only focused on the hyperacute phase, where the delineation of the affected area is more difficult to do (Jones and Cercignani, 2010) . On the other hand, for the thrombi segmentation, (Lucas et al., 2019) learns to segment potential candidates using Unet3D and a Convolutional architecture classifies the candidates. Polar-UNet (Zoetmulder et al., 2022) , uses a CNN to segment thrombi restricting the volume-of-interest to the brainstem, or in (Tolhuisen et al., 2020) patches from two hemispheres are used to segment the thrombi, as a healthy brain tissue should be symmetric. However, all these methods use CT images. For MRis in (Kobold et al., 2019) Multidirectional Unet was used and a recurrent method also denoted as Logic LLSTM (Kobold, 2019) but no distal thrombi were present. 
dataset and preprocessing: 
 Two datasets are used in this study: CHSF (proximal occlusions (P)) and MATAR (distal occlusions (D)). All MRIs come from patients treated for stroke at the Centre Hospitalier Sud-Francilien and were acquired from a 1.5 T and a 3 T GE Healthcare MRI machine in the hyper-acute phase, before treatment. The details of datasets are described in Table 1 . Even though they have been used for several clinical studies (Marnat et al., 2023) , we use only a subset of it, segmented by neurologists. Notice the differences between MATAR and CHSF: the thrombi and lesion size and the ASPECT. Due to registration problems such as missing modalities, only 188 MRIs are used finally. The susceptibility-weighted angiography (SWAN) with its associated PHASE are used to segment the thrombi, and the apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) modalities are used to segment the lesion. The susceptibility images (SWAN and PHASE) have the same geometries and alignment. This is because SWAN is calculated using the machine's PHASE and the magnitude image. It happens the same for the diffusion ones, as ADC is obtained from DWI and B0.The dataset is normalized using Nyul's method (Nyul et al., 2000) . Computing per modality an average histogram using the quantiles of all the data, the MRI's intensities are normalized using that histogram as a reference. The skull-stripped MRIs are used and DWI is coregistered to SWAN using ANTS software, producing images of size 512×512 in the (x,y)-plane and z varies between patients. As input for the model, we take 256×256×s-size crops, where s is the chosen number of slices. To reduce x and y the center of mass is calculated to add around 128 pixels in all dimensions (arriving at a size of 256×256 around that center). As DWI and SWAN have different resolutions and indeed the lesion is normally a bigger region than the thrombi, the crops in z are done in the original resolution, taking the corresponding slices. Having s slices from SWAN and s slices from DWI means that we are seeing a bigger brain region in the diffusion modality, which allows the model to have an increased attention impact.To detect the thrombi, the neurologists look for the lesion as the first step. From the location of the stroke consequence (damaged tissue) they know where to look for its cause (only one hemisphere is affected for example). Trying to mimic that reasoning, we propose AttLLSTM which merges DWI (where the lesion is visible) and SWAN and PHASE (where the thrombus is seen) using cross-attention. An improved version of the CLSTM (Shi et al., 2015) that we denote as Logic long-short time memory (LLSTM) is used to segment the thrombi. The longitudinal direction matches the time using s slices (the prediction is done by calculating the cell and hidden states with the previous slices). Finally, the segmentation is improved by merging the full lesion and thrombi predictions using a post-processing module. The details of AttLLSTM are shown in Fig. 1 .Figure 1 : AttLLSTM. The thrombus is segmented using a cross-attention module between DWI, SWAN and PHASE followed by the LLSTM architecture.The cross-attention module merges the diffusion modality DWI (as it is just to guide where the affected area is, only one diffusion modality is used) and the susceptibility ones (where the thrombi information is). It is computed as in CrossVIT (Chen et al., 2021) . First, we apply a patch embedding (p 1 (DWI), p 2 (SWAN, PHASE)) of the images. We compute Q from the embedding of the diffusion modality and K and V from the embedding of the concatenation of susceptibility ones, normalizing and applying an MLP. The scaled dotproduct attention (Vaswani et al., 2023) is then calculated from Q, K, and V . As p 1 and p 2 are different values in our case, the smaller embedding output (larger patch size) is resampled. The usual Transformer Encoder module is used where a residual connection is done with a normalization layer, followed by an MLP and another residual connection and normalization. After this module, we reshape the output (going back to a 3D image) and a convolution layer (CNN) of size 7×7 is included (having the same output channels as input ones) followed by Elu activation. All these operations are applied per slice (in 2D).Denoting V || l S as the concatenation along the axis l of the two tensors V and S, as • the Hadamard product and as L(A) = W * A + b the classic convolution with a kernel W of size w × w, CLSTM (Shi et al., 2015) is defined by the following equations:A = c t || 4 h t || 4 x t (1) f t = σ(L f (A)) (2) i t = σ(L i (A))(3)EQUATIONd t = tanh(L d (A)) (5) c t+1 = f t • c t + i t • d t (6) h t+1 = o t • tanh(c t+1 ) (7)where x t is the input image of size n 1 × n 2 × 1 × n 4 Input . The cell state c t , which is the model's memory, and the hidden state h t , which is the model's output, are of size n 1 × n 2 × 1 × n 4 hidden . The forget gate f t and input gate i t decide what is erased from and written to the memory c t . The output gate o t determines which parts of the memory are retrieved to create the output. At time t, the model has access to its memory of all previous steps, in our case, the longitudinal direction matches time. To reduce the number The operation is reduced to the concatenation of a convolution part (a 1 ) and a logic part (a 2 ). The convolution operation is only applied on a part of A (A c ), reducing considerably the number of learned parameters. To do so, we first slice h t and c t in h 1,t , h 2,t and c 1,t , c 2,t .EQUATIONi.e. the hidden state h t is split into a convolution part h 1,t with n c channels and a logic part with n l channels and likewise for the cell state c t . Using them we define the splits of A, A c and A l as followingEQUATIONConsidering that L i are convolution layers with weights W i and b i and in particular L 2 has b 2 = 0, we obtain the convolution (a 1 ) and the logic (a l ) part and Logic(A) as following:a 1 = L 1 (A c ) + L 2 (A l ) a 2 = L 4 (T (L 3 (A c ))|| 4 A l ) (10) Logic(A) = a 1 || 4 a 2 (11)where T is put for the Transfer Layer, which produces the same output size as the input size. L 1 , L 3 and L 2 , L 4 are w × w and 1 × 1 convolutions respectively. Finally, denoting(V i 1 i 2 i 3 i 4 ) i k ∈[r 1 ...r 2 ]as the slicing V by setting the dimension i k to a specific range r 1 , . . . , r 2 or a single value r 1 , T is defined as follows:EQUATIONwith i ∈ {1, . . . , k}, j ∈ {1, . . . , m}. I is of dimensions n 1 , n 2 , n 3 = 1, n 4 = n 4 l = km where the multiplicity m is a positive integer. We choose p as powers of 2 using p = 2n 1 2 i . For example, having n 4 = 6 and m=3, we have k= 2 so we apply a pooling layer on the first three features, and another one in the last three ones, each one with different sizes. So, this layer performs a max pooling (Ranzato et al., 2007) operation on each channel separately, with varying window sizes (p).The convolution result (a 1 ) can be seen as the original CLSTM, being applied to part of A (L 1 (A c )) and receiving information from the logical part (L 2 (A l )) as additional bias. The T function moves features across the image in a series of different neighborhood sizes, from local to global. This allows the receptive field of the gates to be as large as the whole input image (which allows doing the concatenation with A l ) and captures the spatial distance as well. In addition, a double pass is done to not start with zero memory and to replicate a bidirectional recurrence method but without adding parameters. The first pass is done to save all the memory states, so the prediction is done using them in the second one (Kobold, 2019) . Finally before the prediction, a convolutional layer is included of size 1×1 with output channels 2 (number of classes).The lesion and thrombi segmentation are merged to reduce the possible false positive thrombi detections. For this purpose, nnUnet (Isensee et al., 2021) is trained using ADC and DWI as inputs (the modalities in which the hyper-acute lesion is seen (Tsai et al., 2014) ), reaching a Dice of 0.72 in average and roughly a 100% percent detection rate. More details about these results are in Appendix A.Two steps are used as postprocessing. First, we choose the object that is closer to the lesion prediction (if there is one), reducing false positives. Using objects with a certain size (the ones smaller than 20 pixels are eliminated), the Euclidean distance between the center of mass of both segmentations is computed. In order to relax constraints, we select the closest object to the lesion with at least N pixels of distance from the others. This produces as output in most cases just one object. Finally, the pixels that are around the predicted one are included reducing the pixel classification threshold (T ), increasing the size of the predicted thrombus.The training configuration is described in Table 2 . We use flipping (in three dimensions) and Gaussian noise (each one is chosen in every iteration with 40% of probability) as data augmentation. As the thrombus is a small object, we use per iteration a crop including the thrombi and another without to manage the unbalanced dataset. We have a sequence of slices containing the full thrombus (successively as the thrombi is a dense object), some other slices just before, and after the thrombus (all of them without thrombi). So per iteration, one crop of s slices is chosen from the first group of crops and another from the other two, both for the same patient. The metrics used are the Dice score, which is calculated pixel-wise and measures the overlap between the prediction and the ground truth, the average count and size of false positives (FP) and false negatives (FN) (in pixel count), and the detection rate (det.) calculated by patients, being one if at least one pixel's prediction overlaps the ground truth. 
results: 
 As the number of slices is a critical hyper-parameter on the recurrent methods presented (in nnUnet the software chooses the crops by itself), a specific study on that parameter is done. The results using several slices are in Table 3 for proximal (P) and distal (D) occlusions. Using s equal to 12 for all the recurrent methods, as it produces the best result using AttLLSTM, we compare the results with some of the SOTA methods for the thrombi segmentation in Table 4 . nnUnet is trained using the general procedure and all our contributions are tested: first, just LLSTM and CLSTM (adding at the beginning a CNN with 5×5 filters instead of the attention), the attention module, and the two post-processing techniques (lesion and threshold (Thr)). As the other methods proposed for the thrombi use the hemisphere position (Tolhuisen et al., 2020) or the reference path through the brainstem as annotations (Zoetmulder et al., 2022) , we do not include them. nnUnet struggles to segment the thrombi: less than 80% of the patients are detected. LLSTM outperforms CLSTM in the Dice score, even though the detection rate is lower. Adding to both recurrent methods the attention module increases the performance. AttLL-STM has the highest detection rate (having 91% in the smaller cases) and Dice. Without using the distal occlusions, AttLLSTM misses 40% of the patients and including them in the training improves all metrics. For the post-processing techniques, adding the lesion (Lesion) reduces the number of false positives in more than half of them but also reduces the detection rate for distal cases (some wrong close objects are chosen) for AttCLSTM.  
conclusion: 
 We have proposed a model relating the lesion and the thrombi segmentation on MRIs that outperforms the other methods tested. A cross-attention between the modalities as a recurrent model where the longitudinal direction matches time and a way of merging the lesion and the thrombus is defined. AttLLSTM detects almost all thrombi present in stroke patients in less than 3 minutes, producing promising results in the hyper-acute phase, for distal and proximal clots. This detection could save time, help to choose a treatment or estimate the survival of the patients.But, due to the lack of public datasets, the multicentric robustness cannot be tested, even though augmentations and normalizations have been defined to overcome that problem. MRIs from different vendors will not only produce different images due to calibration but the susceptibility modalities can also be different: SWI instead of SWAN. The two postprocessing proposed techniques could be done differently: for example, merging the lesion and thrombus with a learning method or improving the Dice with another technique. 
 
 Appendix:  


Decision: The reviewers appreciated the technical novelty, in particular the use of cross-attention, as well as the clininical relevance of the approach. The authors improved the manuscript after rebuttal.

Reviews: review: paper_summary: paper_summary: In this work, the author introduced a attention based CLSTM model for segmenting the thrombus on the MRI of patients with hyper-acute ischemic stroke. It is significant to note that the inclusion of multiple modalities, such as DWI, SWAN, PHASE are included in a cross-attention manner. It was proposed that with the cross attentioN, CLSTM, post-processing, a over 0.6 DICE score with few False positive can be achieved for this segmentation task.
main_review: main_review: After the revision, my questions are resolved. The author added the required figure showing the design of the work, provided more detailed logic of LSTM used in the brain MRI, and explained the reason of 'slicing' parameters to make the paper more complete. Overall, this work includes the multi-modality and the use LSTM in 3D MRI image segmentation, which is innovative for the community.
strength_weakness: strength_weakness: This is an interesting work, however, the organization and figure of the Methodology could be improved. First of all, the paper lacks a overall/detailed figure showing the design of the work. For example, it is not clear for me to fully understand how the modified recurrent network is used in Figure 1. Section 2.2.2 provides detailed logic of LSTM, however, it is unclear to connect it with Figure 1. Secondly, it is interesting to see the author proposes a way to use the slice (s) dimension as the time (t) in recurrent model. However, these related logic (equations in 2.2.2) is better to be related with a visualization.
questions: questions: 1. In the chapter 2.2 Methodology, Figure 1 (Proposed method) is clear for presenting the overview of fusing different modality together. However, it is not clear to relate it to the remaining content of the chapter 2.2. It is better to provide a more detailed figure to show the design of the network model, the post processing module.

2. From the content of 2.3 and Table 2, "A patient’s MRI is divided into three crops: the crop that involves all the thrombi, the one with all the slices before it, and the one after it." It was not clearly stated the reason for the split.
paper_summary: paper_summary: The paper addresses the problem of segmenting the thrombus within MRI images of patients suffering from acute ischemic stroke. Multimodal MRI are used as input (ADC, DWI, SWAN and PHASE). A lighter version of Convolution LSTM is proposed through the definition of a Logic operation. The induced Logic LSTM (LLSTM) model is used to segment the lesion from ADC and DWI images on one hand, and the thrombi from DWI, SWAN and PHASE images on the other hand, after these modalities have been combined through a cross attention module. Three post-processing steps are further applied: proximity of the thrombus to the lesion, and hysteresis thresholding, together with size filtering. The method is tested on two datasets including both proximal and distal thrombi (resp. 63 and 125 patients) and is shown to outperform both nnUnet and CLSTM, with very few detection errors.
main_review: main_review: I'd like to thank and commend the authors for the work they have put in the revised paper. In my opinion, the paper is much easier to read and apprehend. However I still have some concerns about the significance of the reported results and the actual impact of the attention to lesion in the proposed model, which exists indeed, but still requires to be boosted by post-processing.
strength_weakness: strength_weakness: The paper is very difficult to read, and its writing should be dramatically improved. Overall some critical information is missing, and many other pieces of information appear to be scattered in the text. A careful re-reading might help reorganize these to improve the text. The main points are:
- MRI modalities are referred to by acronyms only. The authors should be aware that their paper might be of interest to researchers looking for methods to segment small objects from multiple modalities, which is a broader audience than neuroradiologists interested in thrombi delineation to treat AIS. Furthermore, information about their complementarity should be presented at the beginning of the paper (instead of the first sentence of section 2.2.1 for example). Also, why just use DWI for cross attention and not embed DWI and ADC together (the first sentence of 2.2.1 is about diffusion modalities (plural))?
- the databases are partially described but this should be improved. The reader should not have to read a clinical publication to understand what MATAR is, or the conditions to access the data. DWI and SWAN image geometries are provided but what about ADC and PHASE? Are there healthy patients in the cohorts?
- Section 1.2 provides a rather complete overview of the previous methods for lesion segmentation, but it is mainly descriptive. What are the limitations of these methods? Why discard these methods and only compare to nnUnet in section 3.1? The separation between lesion and thrombus segmentation is unclear.
- ISLES uses FLAIR and DWI for lesion segmentation. The authors use ADC and DWI. Is it merely practical (modalities present in the databases) or are there more fundamental reasons?
- see detailed comments for more examples. 

In the LSTM model, the longitudiinal direction serves as time. What about causality? Is there a difference between top to bottom or bottom to top directions? Would there be an interest in coupling both derived models (in an ensemble way for example)?

A lot of preprocessing is performed (see end of section 2.1). I suppose it is required at inference time. Is it fully automatic? Does it require some manual intervention? In such cases, is it compatible with the clinical emergency setting? Cropping operations are also somewhat unclear. For example in section 2.3 the authors refer to "the crop that involves all the thrombi". Is it always possible with 12 slices? Is it always unique? How are these crop operations involved at inference time?

In section 3.1, nnUnet is better than LLSTM to segment the lesion. But in section 3.2, AttLLSTM is better for thrombus segmentation, in particular if both proposed post-processing steps are applied. These steps seem to be applicable to the result of nnUnet, exploiting the segmentation of the lesion by nnUnet. Unless I am mistaken, this configuration (nnUnet + Lesion + Thr) should be included in the Table 4. Also both the attention module and post-processing steps are compatible with CLSTM. The induced configurations should be included in Table 4.

- Reproducibility (optional): it would be very helpful to better grasp the details of the proposed model if a code would be available to the community
questions: questions: - propositions to improve the readability of the paper (see weaknesses above)
- discuss whether section 3.1 is actually useful. If not, expand section 3.2 with results including nnUnet + Lesion + Thr, and a version of AttCLSTM (+ Lesion + Thr). Unless I am wrong and the latter combination is impossible.
paper_summary: paper_summary: The authors modified Convolutional Long-short-term Memory (CLSTM) and built Logic long-short time memory (LLSTM) as recurrent network for segmenting thrombus on brain MRI in patients with hyper-acute ischemic stroke. This paper utilizes two datasets: MATAR (distal) and CHSF (proximal).
main_review: main_review: The authors have addressed most of my questions, and they have also made some adjustments in their Appendix. However, the dataset's size remains a limitation, making it challenging to draw definitive conclusions.
strength_weakness: strength_weakness: 1. In section 2.1, the CHSF dataset is explained in detail, whereas the description for the MATAR dataset is limited to Table 1;
2. Combining both datasets, they only have 188 data points. Even after splitting the data into training, validation, and testing sets, the dataset remains very small;
3. If the dataset is too small, it is hard to understand the robustness of the proposed framework; The authors could evaluate their framework on more target datasets;
4. Table 2 displays hyperparameters, but a more thorough ablation study is necessary. For instance, is selecting only 12 slices sufficient? Furthermore, how were these 12 slices chosen?
5. In Table 3 and 4, does "Both" mean utilizing MATAR and CHSF datasets together?
6. The performance for the proposed AttLLSTM is not showed for lesion detection.
7. Did the author consider utilizing vision transformers instead of LSTM? Can vision transformer perform better than LSTM here?
8. The authors mentioned couple of methods in their related study. I would suggest authors to compare their proposed framework with other state-of-the-art methods.
questions: questions: Please check the points mentioned in the section "Weaknesses".

Comments: title: Rebuttal to reviewer 3
Rebuttal to reviewer 2
Rebuttal to reviewer 1
comment: Dear Reviewer,

Thank you again for your remarks. Even though our database is considerably small in terms of the number of patients, we tested all the possible configurations and we include the results for the control MRIs in the appendix, to show how robust is our proposal. The inclusion of the lesion distance is needed as we do not have zero positives and this is key for some next studies (thrombus length, etiology, cause, etc). With some few false positives, these studies could not be done. But in any case, the attention module helps in all the cases (CLSTM and LLSTM) even more than the lesion distance (for AttCLSTM, the lesion distance reduces the detection rate as if there are several false positives, the choice of the closest thrombus to the lesion might be wrong, as the relation between them is not as simple as the closest one). 

Concerning the remarks:

- Done
- Done
- The sizes are now shown in mm3
- The order CHSF and MATAR is now consistent in all sections.
- Done
- Done
- Done
I want to thank the authors for the extensive work that has been done to take my comments into account and improve their paper. I feel that it is much clearer and easy to read now. The rationale and the imaging modalities are now clear, Figure 1 is way more informative, so is the addition of Figure 2. 

However, I conquer with one of my fellow reviewers that the size of the database does not allow for any definite answer, even though a positive trend appears in the reported results. Furthermore, the impact of the attention module is difficult to circumscribe: it is supposed to leverage the lesion location to help locating the thrombus, but still, selecting the closest detection to the lesion is a necessary post-processing step. This somewhat mitigates the impact of the attention module in my opinion.

I have other editing suggestions to make:
- p. 2, section 1.1, l1: As the lesion is present brighter -> As the lesion is brighter
- use "larger" instead of "bigger" to describe patches
- in Table 1, I do not reckon that pixel count is an appropriate metric when images of different resolutions are used. I think pixels are taken in SWAN (PHASE) images for the thrombus, but the lesion is segmented from DWI+ADC which have a different resolution. Using pixel count for both objects does not seem very appropriate to me.
- Also, in the same table, as well as in the text, I would reference CHSF first, and then MATAR. This would help the reader make the connection with P/D used later if the same order is kept consistent (CHSF corresponds to P(roximal thrombi) and MATAR corresponds to D(istal thrombi). The same idea could be kept later, e.g. in table 4 where CHSF+MATAR could be used instead of MATAR+CHSF, to be consistent with P+D.
- In section 2.2, on page 3, the following sentence is hard to read: "An improved version of the CLSTM... with the previous slices). Please consider splitting it in two: a first sentence to introduce LLSTM, used to segment thrombi; and a second to specify that time is replaced by (matched with) the longitudinal direction, where s slices are considered.
- First two columns in table 3 could be removed and the AttLLSTM/MATAR+CHSF setup could be mentioned in the caption.
- Adding MD-UNet as another reference method is good, but I cannot find any interest in presenting results only on the CHSF database. The only result on the CHSF database of interest is that of AttLLSTM + Lesion + Thr, which is not reported. But AttLLSTM alone, as done by the authors, enables to highlight the interest of the merged database. In Table 4, I feel the MD-Unet line could be removed, and nnUnet+Lesion+Thr from the appendix should be added, together with AttCLSTM+Lesion+Thr if possible.
Thank you, my questions have been resolved
Dear Reviewer

Thank you very much for all the remarks. Concerning the  weaknesses of our text:

- All section 2.1 talks about MATAR and CHSF, I think the misunderstanding was because the hospital (Centre Hospitalier Sud Francilien) acronym and the name of the first dataset "CHSF" were the same. Now it is clarified in section 2.1.  More information about the dataset (description of geometries, modalities, the groundtruths etc) is provided.

- It is true that we just disposed of 188 patients but several experiments were done (all possible configurations are now available either in Appendix B or Table 4. Also, the evaluation on control MRIs are included now in Appendix C (in 30 more patients).

- As the proposal is specific for thrombus detection, as the biological relationship between the lesion and the thrombi are key points of the model and prediction, we cannot use another target dataset. There is no public dataset for this purpose but as mentioned before now the evaluation on control MRIs is added including additional configurations and also a SOTA model to assess the model's robustness.

- Indeed, the number of slices is a critical hyperparameter, we have added. Table 3 presents the experiments done to optimize its choice (section 3). 

- Yes, "both" means MATAR and CHSF. Both names appear explicitly now in the tables  to clarify this.


- AttLLSTM is only for thrombus segmentation. It uses a set of MR sequences and it cannot be used for lesion segmentation as the idea of this architecture is to guide the thrombi prediction (which is more difficult due to its size) by using the lesion (bigger and easier to segment) imitating the neurologist procedure. Knowing where the lesion is they look for the thrombus. As the lesion section was confusing and not many experiments were done, as it is just used as a support, the results are now just mentioned in the methodology (section 2.2.3) and all the details are in Appendix A

- About vision transformers, due to the lack of data and the immense size of the architecture, we decided to not test it. Our proposal tries to imitate a part of its mechanism (the attention module) but with a lighter architecture, to reduce the overfitting risk.

- Some of the state-of-the-art methods use other information as input (brain hemisphere or brainstem) that we do not have and in addition, we wanted to do a model free of these annotations (as they are timely and expensive). But the comparison with Multidirectional Unet is now included in Table 4 as well as all the possible configurations.
Dear Reviewer,

First of all, thank you for all the remarks and details mentioned in the review. We have made several improvements and the detailed comments are mentioned below:

- MRI modalities:  all acronyms are included now, the explanation of why each modality is used and their complementarity in section 2.1 and why only DWI is used for the attention module in section 2.2.1

- Database description: section 2.1 describes both datasets (MATAR and CHSF), I think it was not clear to include the same acronym for the hospital (CHSF) and the dataset CHSF. All MRIs come from Centre Hospitalier Sud Francilien with similar conditions. This clarification is now included as well as the modality geometries. All the patients had a stroke but the control MRIs are available only for the distal ones, the results on these MRIs are now included in the appendix.

- Section 1.2: the limitations of the lesion segmentation methods as well as a clear separation between lesion and thrombus are precise in the new paper version. Not all the lesion methods are tested as the main contribution of the paper is the thrombus segmentation, in this new version of the paper the main objective is clear.

- Why ADC and DWI? The lesion delineation is done based on ADC and DWI in the hyper-acute phase. When there are old lesions they are visible in FLAIR, probably that's why it is also included in some studies (when there are strokes after several months), but it is not our objective as we are focusing on the actual brain lesions segmentations. This clarification is now included in section 2.1 and when the lesion segmentation results are shown in section 2.2.3

-Causality: the model was trained by doing a double pass to imitate the bidirectional LLSTM without adding parameters to train: with the first one the memory is calculated to be used for the prediction using the second pass (it is mentioned at the end of the section 2.2.2)

- Preprocessing: the preprocessing is fully automatic, the modality registration using ANTS is the longest stage (up to 3 minutes, depending on the patient) but it is compatible with the clinical emergency setting. As the thrombus is a small object, we use per iteration a crop including the thrombi and another without to manage the unbalanced dataset. We have a sequence of slices containing the full thrombus (successively as the thrombi is a dense object), some other slices just before, and after the thrombus (all of them without thrombi). So per iteration, one crop of 12 slices is chosen from the first group of crops and another from the other two, both for the same patient. This clarification is now included in section 2.3 and why 12 slices are chosen in section 3. The number of slices is a hyperparameter chosen as it was the best one obtained after several experiments (table 3 in section 3). Not always with 12 slices is all the thrombi is inside, so each time a crop center is chosen randomly and used.

- More configurations: AttCLSTM results are now available in table 4 and for the post-processing methods, as the methods can only improve the Dice and reduce the false positives, we show in table 4 only the results for the best model (especially having a higher detection rate) but all the configurations and results are now included in appendix B (page 12), as it is possible to include them.


- General electrics is now replaced by GE Healthcare

- CLSTM is correctly referenced now

- Figure 1 is improved giving all the details now

- The sentence "the time t becomes the slices along the axis" is replaced by "longitudinal direction matches time" and included at the beginning of 2.2 and illustrated in Figure 1

- Not many experiments were indeed done for the lesion as it is only used as support for the thrombus detection, so the section for the lesion results is canceled and the results are mentioned in section 2.2.3 (with all the details  in Appendix A)

- The units are now clarified in section 2.3 (metrics)

- AttLLSTM is now mentioned from the beginning in the methodology section (section 2)

- The mishaps are solved now

About the questions to address

- The readability of the paper has improved with all the changes done and thanks to the figures added

- Section 3.1 is now in the appendix but the model used is mentioned in the methodology. The experiments of why 12 slices are chosen and the results using AttCLSTM are now in section 3. All the configurations with the post-processing (nnunet+lesion+thr, attclstm +lesion+thr) are available in the appendix
Dear Reviewer,

Thank you very much for the remarks and for the detailed comments, which are possible questions to address for the next research steps. We have made several improvements taking into account the questions to address in the rebuttal mentioned:

- Methodology: we have included an improved overall figure including all the details of the AttLLSTM architecture (page 4). Also, a figure explaining the Logic operation is now included (page 5)

- Crop split: the explanation of the crops was not clear in the previous paper version. As the thrombus is a small object,  to manage the unbalanced dataset, per iteration we use a crop arround the thrombi and another without. Per patient, we have the slices containing all the thrombi (all followed by each other as the thrombi is a dense object), the slices just before that, and the ones after that (all of them without thrombi). So per iteration, one crop of 12 slices is chosen from the first group of crops and another from the other two, both for the same patient. This clarification is now included in section 2.3