Synapse multi organ segmentation dataset. 8137 on Synapse multi-organ segmentation dataset.


Synapse multi organ segmentation dataset 73% and AFFSegNet consistently achieves high scores across all organs, demonstrating its ability to generalize to different anatomical structures. Following [2, 34], 18 samples are divided into the training set and 12 samples into testing set. Specifically: (1) DSG-ViT with MTA ablation experiments; (2) the effect of the number of skip connections; (3) The effect of input resolution; (4) The effect of the number of DSG-ViT modules. Sep 27, 2022 · Synapse is a multi-organ segmentation dataset containing 30 abdominal clinical CT cases. 68% and 91. On the ACDC dataset, our model achieved a DSC of 92. Each CT image contains 85–198 slices of 512 × 512 pixels. The experimental results show that RotU-Net with a very small number of parameters achieves impressive performance, which demonstrates the effectiveness and efficiency Mar 1, 2024 · The experiments are first conducted on the preprocessed Synapse multi-organ segmentation dataset, and the comparative results on the test sets are provided in Table 1 for many advanced segmentation models, such as SwinT-Unet(2022) [41], DSTUNet(2022) [42], FCT(2023) [43], TransDeeplab(2022) [26], TransUNet [28], MTUNet (2022) [44]. Oct 1, 2024 · Extensive experiments and interpretability analysis are made on the Synapse multi-organ dataset (Synapse) and the ACDC cardiac multi-structure dataset (ACDC). Each Feb 18, 2023 · The segmentation results of different methods on the Synapse multi-organ CT dataset are shown in Fig. Feb 4, 2022 · 上の図には、Synapse multi-organ segmentation datasetにおいて、実際にセグメンテーションを行ったときのセグメンテーション画像を示しています。 TransUNetは他のモデルと比較すると、より正確なセグメンテーションを行えていることがわかります。 Synapse is a platform for supporting scientific collaborations centered around shared biomedical data sets. The pancreas marked as yellow in the figure is represented in a small region. We report the Dice score and 95% Hausdorff Distance (HD) on abdominal organs (aorta, gallbladder, spleen, left kidney, right kidney, liver, pancreas, spleen and stomach) as Nov 25, 2024 · Our approach is evaluated on the Synapse multi-organ segmentation dataset, achieving a 6. , only a subset of organs are annotated. We fine-tune Swin UNETR with pre-trained weights on two publicly available benchmarks of Medical Segmentation Decathlon (MSD) and the Beyond the Cranial Vault (BTCV). It can be seen from the figure that CNN-based methods tend to have over-segmentation problems, which may be caused by the locality of convolution operation. Aug 25, 2024 · We validate our proposed MSVM-Net on the Synapse multi-organ dataset and the ACDC dataset. Due to the dense distribution of abdominal organs and the close connection between each organ, the accuracy of the Jan 23, 2023 · Synapse abdominal multi-organ dataset: this dataset includes 30 CT volumes of abdominal organs, with a total of 3779 slices. The results on the ACDC dataset, as shown in Table 4. 02237, 2018. Traditional convolutional neural network (CNN)-based approaches struggled to achieve precise segmentation results due to their limited receptive fields, particularly in cases involving multi-organ segmenta-tion with varying shapes and sizes. Dec 2, 2024 · Experimental results demonstrate that our UNeXt++ outperforms UNeXt in terms of segmentation performance on the multi-organ segmentation dataset Synapse and three single-organ segmentation datasets. 54 ] × [ 0 . Specifically, on the Synapse multi-organ dataset, our model achieved a DSC of 85. The data comprises reference segmentations for 90 abdominal CT images delineating multiple organs: the Jan 1, 2025 · The synapse multi-organ segmentation dataset includes 30 CT scans from the MICCAI 2015 Multi-Atlas Abdominal Organ Segmentation Challenging, encompassing a total of 3779 abdominal CT images. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Eighteen specimens were assigned to the training dataset and the remaining 12 specimens were assigned to the testing dataset. Jun 1, 2022 · Experiments on the synapse multi-organ segmentation dataset and the ISIC2017 skin lesion challenge dataset have demonstrated the superiority of our method compared to other state-of-the-art segmentation methods. Best results are in bold. Load Nifti image with metadata, load a list of images and stack them. The annotation of each image includes 8 abdominal organs. On the Synapse dataset, compared to SwinUnet , MS-UNet achieved a \(1. There are 30 contrast-enhanced abdominal clinical CT cases in this dataset. From the results, it can be seen that the proposed MS-UNet exhibits overall better performance than the other methods. Aug 31, 2024 · Table 3 Ablation studies were conducted on the Synapse multi-organ segmentation dataset. 98 , 0 . With a diverse set of annotations that cover multiple organs, such as spleen, pancreas, gallbladder, and others, the synapse dataset is a complex multi-class segmentation challenge. 1. The best result for 2D and 3D models within each column is highlighted by bold , and the second-best is highlighted with an underline . Synapse is a medical dataset for the MICCAI 2015 Multi-Atlas Abdomen Labelling Challenge. To evaluate the performance of our model, we conducted experiments on two datasets: UW-Madison Gastrointestinal Segmentation and Synapse Multi-organ Segmentation. Nov 8, 2024 · Synapse multi-organ segmentation dataset (Synapse): Synapse [24, 45] is a publicly available multi-organ segmentation dataset comprising 30 abdominal CT cases with 3,779 axial abdominal clinical CT images, including 8 types of abdominal organs (aorta, gallbladder, left kidney, right kidney, liver, pancreas, spleen, stomach). 'Multi-Atlas Labeling Beyond the Cranial Vault - Workshop and Challenge' (Synapse ID: syn3193805) is a project on Synapse. INTRODUCTION Multi-organ medical image segmentation, which Sep 26, 2024 · Table 1, Table 2 and Table 3 show the segmentation results of MS-UNet and other up-to-date methods on the Synapse multi-organ CT dataset, ACDC dataset, and JSRT dataset. The dataset has 30 cases with 3779 axial clinical CT images of the abdomen. Jun 16, 2022 · Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Our proposed UNETR++ achieves favorable segmentation performance against existing methods, while being considerably reducing the model complexity. Approximately half of all cancer patients undergo RT. Following the settings in , 18 cases are used for training and 12 for testing. 89 mm HD95 scores. 98 ∼ 0. Swin-UNet[30] employed Swin Transformer blocks to construct encoders and decoders in an UNet-like architecture, showing improved performance compared to TransUNet[31]. A segmentation head is attached at the end of the decoder for computing the final segmentation output. Therefore, it is crucial to investigate how to learn a unified model on the available Jan 7, 2025 · Synapse Multi-Organ Segmentation Dataset (Synapse): Synapse dataset is a publicly accessible resource designed for the segmentation of multiple organs. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation[J]. 2%, 84. 2. While ASSNet achieves state-of-the-art results with an average DSC of 90. 4. AFFSegNet achieves state-of-the-art results with an average DSC of 90. Prepare an virtual environment with python>=3. Conventional CNN has achieved great success in general medical image segmentation tasks, but it has feature loss in the feature extraction part and lacks the ability to explicitly model remote dependencies, which makes it difficult to Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. The u-shaped architecture, popularly known as U-Net, has proven highly successful for various medical image segmentation tasks. testing). By using Dice Similarity Coefficient (DSC) and Hausdorff_95 (HD95) as the evaluation metric for the Synapse dataset. The Synapse dataset [15] provides 30 annotated CT images. Our trained SAMed model achieves 81. See a full comparison of 7 papers with code. Architectures Qualitative Results on Synapse Multi-organ dataset Synapse Multi-Organ Segmentation Dataset Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. Dec 26, 2023 · From the results, it could be seen that the proposed MS-UNet exhibits overall better performance than others. May 1, 2023 · We conducted ablation experiments under the Synapse multi-organ segmentation dataset to investigate the effect of different factors on the model. To use a cross entropy loss, the model should typically predict the probability distribution for all labels in the dataset as Dec 28, 2024 · We conduct experiments on the synapse multi-organ segmentation dataset 1 [69] involved in the MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge. It can be seen that adding each module to the model Oct 10, 2022 · July 14, 2022: First release (Complete implemenation for Synapse Multi-Organ Segmentation dataset. 9% improvement in Mean Dice score and a 39. We used 18 cases as the training set and the left 12 cases as the test set. py Citation @article{chen2024msvmunet, title={MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation}, author={Chaowei Chen and Li Yu and Shiquan Min and Shunfang Wang}, journal={arXiv preprint arXiv:2408. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with Jan 15, 2024 · Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. 1 In our experiment, we randomly crop the scans into the size of 48 × 192 × 192 as the input. Oct 8, 2023 · Multi-organ segmentation, as an upstream task for many medical tasks, has been applied to many real clinical scenarios with great success. 0]) mm 3. 54] × [0. Our proposed ASF-LKUNet achieves 88. # Synapse Multi-Organ Dataset python train_synapse. 96M parameters and 29. Each case consists of 85 to 198 images of pixels. with the multi-organ-segmentation topic This tutorial demonstrates how to construct a training workflow of UNETR [1] on multi-organ segmentation task using the BTCV challenge dataset. V-net: Fully convolutional neural networks for volumetric medical image segmentation[C]//3D Vision (3DV), 2016 Fourth International Conference on. This tutorial uses a Swin UNETR [1] model for the task of multi-organ segmentation task using the BTCV challenge dataset. Table 1: Performance comparison with 2D and 3D segmentation networks in the multi-organ segmentation using the Synapse multi-organ segmentation dataset. then connected to a CNN-based decoder. 1% on cardiac, abdominal Dec 29, 2022 · An ablation study is implemented to evaluate the fusion module’s effectiveness and skip connection on the synapse multi-organ segmentation dataset. 1 Dataset 4. Multi-organ segmentation over multiple datasets. arXiv preprint arXiv:1806. Feb 6, 2024 · Finally, we have validated RotU-Net on the Synapse Multi-Organ Segmentation Dataset (Synapse) and the Segmentation of Multiple Myeloma Plasma Cells in Microscopic Images (SegPC). For both datasets, we employ simple data augmentation such as random rotation and flipping. 64 HD on the Synapse multi-organ segmentation dataset,whichisonparwiththestate-of-the-artmethods. This is the implementation of Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation, MIDL 2023 Video. See a full comparison of 20 papers with code. 00% and an HD95 of 14. As shown, the proposed MAXFormer obtained the best 83. It includes 30 abdominal CT cases and contains 3779 axial CT images in total. 21\%\) improvement in DSC and a \(6. When the SideConv module is removed Feb 7, 2023 · Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. Synapse: Synapse is a public multi-organ segmentation dataset. Each CT scan consists of 85 to 198 slices with 512 × 512 pixels each, and each voxel measures ( [ 0 . 98 ] × [ 2 . Oct 28, 2024 · Our proposed model shows superior performance, effectiveness and robustness compared to SOTA methods, with mean Dice Similarity Coefficient scores of 92. Define a new transform according to MONAI transform API. Our model is currently the Jun 25, 2024 · Multi-organ Synapse dataset: is a multi-organ dataset, includes 30 abdominal CT scans with 18 train and 12 validation scans. Following the settings in [7], 18 cases are used for training and 12 for testing. Mar 27, 2024 · On the Synapse multi-organ CT dataset, H-SAM performs precise segmentation for small-scale organs. 45% DSC scores on the Synapse and ACDC datasets, respectively, with 17. org Apr 26, 2023 · Different from SAM, SAMed could perform semantic segmentation on medical images. Download scientific diagram | Sample 2D slices of the Synapse multi-organ segmentation dataset and the multi-organ annotation mask. 1 Dataset. 2 visualizes segmentation results of different methods on the Synapse multi-organ CT dataset. And the segmentation effect of MAXFormer on the six organs (gallbladder, left kidney, right kidney, liver, pancreas, and spleen) is also better than other Synapse is a platform for supporting scientific collaborations centered around shared biomedical data sets. 5. 58%. However, medical image datasets are often low in sample size and only partially labeled, i. Nov 28, 2022 · The Synapse Multi-organ Segmentation dataset, 1 Gland Segmentation dataset (GlaS) [43] and Automated Cardiac Diagnosis Challenge dataset (ACDC) [44] are used to evaluate the proposed model. The 50 scans were captured during portal venous contrast phase with variable volume sizes (512 x 512 x 85 - 512 x 512 x 198) and field of views (approx. 1 Synapse multi-organ segmentation dataset. In our experiments, 18 cases are used for the training of the model, and the rest 12 cases for testing. The ablation experiments also prove the generalization performance of TransClaw U-Net. The current state-of-the-art on Synapse multi-organ CT is Medical SAM Adapter. 8137 on Synapse multi-organ segmentation dataset. In terms of Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) metrics, GCtx-UNet outperformed CNN-based and Transformer-based approaches, with notable gains in the segmentation of complex and Sep 4, 2024 · Comparisons of the segmentation results between the proposed MSR-UNet and other methods conducted on the Synapse multi-organ CT dataset are shown in Table 1 (note that the results of the contrastive methods are obtained directly from the original articles except for MISSFormer, and the best results are highlighted in bold). py # ACDC Dataset python train_acdc. 64 HD on the Synapse multi-organ segmentation dataset, which is on par with the state-of-the-art methods. SUnet achieves an average Dice of 84. Our goal is to make biomedical research more transparent, more reproducible, and more accessible to a broader audience of scientists. e. from publication: TransNorm: Transformer Provides a Strong The current state-of-the-art on MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge is MERIT. This is the official repository of "AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?". 9% improvement in Hausdorff Distance (HD95) over an implementation without our enhancements. 98] × [2. Feb 8, 2021 · TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK EXTRA DATA REMOVE; Medical Image Segmentation Sep 6, 2023 · To increase the data diversity for the Synapse multi-organ segmentation dataset, augmentation was performed for the training dataset using rotation and image flipping with a probability of 0. Mar 17, 2024 · Synapse multi-organ segmentation dataset (Synapse): the dataset includes 30 cases with 3779 axial abdominal clinical CT images. Fig. In addition, based on the segmentation network, the performance of the classification network has also been greatly improved. Sep 30, 2023 · To evaluate the importance and effectiveness of each component in our model and better understand their behaviors and performances, we conducted ablation experiments on the Synapse Abdomen Multi-Organ and ACDC Heart Segmentation datasets, and the results are shown in Table 3 and Table 4. The dataset includes 30 abdomen CT scans with enhanced contrasts, and it was processed to generate 3779 small volumes with the size of 512 × 512 × 3. In terms of the use of the data set, like other models, we divided 18 samples into a training set (2212 axial slices) and agnostic purposes, including cell segmentation, tumor iden-tification, and organ localization. And it contains the following features: Transforms for dictionary format data. Oct 18, 2023 · We evaluate the performance of the proposed model on synapse multiple organ segmentation dataset and automated cardiac diagnostic challenge dataset. Dec 29, 2022 · our method on the synapse multi-organ CT dataset and the automated cardiac diagnosis challenge (ACDC) dataset. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with Dec 9, 2022 · State-of-the-art comparison on the abdominal multi-organ Synapse dataset. We discuss the impact of fusion modules, the number of skip connections, and the input image’s size on the proposed method’s performance. 66% DSC and 15. Sep 26, 2022 · Synapse (Synapse multi-organ segmentation dataset) 1: The dataset included scans of 30 abdominal clinical CT cases. We conduct extensive experiments to validate the effectiveness of our design. Under Institutional Review Board (IRB) supervision, 50 abdomen CT scans of were randomly selected from a combination of an ongoing colorectal cancer chemotherapy trial, and a retrospective ventral hernia study. Sep 26, 2022 · Results: We performed multi-organ segmentation on CT images and MRI images provided by two public datasets, Synapse multi-organ CT dataset (Synapse) and Automated cardiac diagnosis challenge dataset (ACDC). Jan 29, 2024 · We conduct extensive ablation experiments on the abdominal multi-organ dataset Synapse to validate the effectiveness of the key components of our method. Jun 19, 2023 · 4. 6, and then use the following command line for the dependencies. Access to the synapse multi-organ dataset: Sign up in the official Synapse website and download the dataset. INTRODUCTION Radiation therapy (RT) is one of the most effective cancer treatments. ) This code has been implemented in python language using Pytorch library and tested in ubuntu OS, though should be compatible with related environment. It contains the segmentation task for eight organs including the liver, right kidney, left kidney, pancreas, gall bladder, stomach, spleen, and aorta. The dataset can be downloaded at https://www. The architecture of Swin UNETR is demonstrated as below The following features are included in this tutorial: Dec 29, 2022 · We extensively evaluate our method on the synapse multi-organ CT dataset and the automated cardiac diagnosis challenge (ACDC) dataset. Dec 2, 2024 · transformer segmentation deeplearning wavelet multi-organ-segmentation high-frequency isic-2018 synapse-dataset. In addition, some researchers have developed segmentation models using pure Transformers. 13735}, year={2024} } Oct 1, 2023 · This approach facilitated the training of each decoder layer by effectively utilizing the features extracted at different scales to guide network training. 12) cases are used for training ( resp. The dataset is split into 18 training and 12 testing samples. 1. However, the existing multi-organ segmentation algorithms, which require either amount of real patient datasets or centralized Feb 1, 2024 · We evaluate the performance of the proposed model on synapse multiple organ segmentation dataset and automated cardiac diagnostic challenge dataset. Synapse performed a total of 30 abdominal CT scans (3779 CT images) and delineated 8 abdominal organs (aorta, gallbladder, spleen, left kidney, right kidney 3. Synapse multi-organ segmentation dataset - We use 30 abdominal CT scans in the MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge, with 3779 axial contrast-enhanced abdominal clinical CT images in total. Jul 1, 2023 · The public dataset is provided by Synapse multi-organ segmentation dataset. 75mm. 3. 5 , 5 Oct 1, 2023 · Multi-organ segmentation of the abdominal region plays a vital role in clinical such as organ quantification, surgical planning, and disease diagnosis. May 1, 2024 · Synapse Multi-Organ Segmentation Dataset: This dataset contains 30 abdominal CT scans and 3779 axial clinical CT images of the abdomen, encompassing all 8 organs-the aorta, spleen, right kidney, left kidney, gallbladder, liver, stomach and pancreas. As in the dataset set in [8] and [34] , 18 of these samples were randomly used as a training set for the network, while the remaining 12 samples were used as a test set. Feb 22, 2018 · DenseVNet Multi-organ Segmentation on Abdominal CT This dataset includes the multi-organ abdominal CT reference segmentations publicly released in conjunction with the IEEE Transactions on Medical Imaging paper "Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks" [1]. This dataset contains 3779 axial contrast-enhanced images from 30 abdominal CT scans, where 18 ( resp. Milletari F, Navab N, Ahmadi S A. 41% and 89. synapse. Different from SAM, SAMed could perform semantic segmentation on medical images. Sign up in the official Synapse website and download the dataset. In this work, we collect a large and diverse abdominal CT organ segmentation dataset with 1000+ CT scans by augmenting the existing single organ datasets[1-6] with four abdominal organs, including liver, kidney, spleen, and pancreas. The annotation of each image includes 8 abdominal organs (aorta, gallblad- 81. Synapse is a platform for supporting scientific collaborations centered around shared biomedical data sets. Index Terms—Organs at risk, head and neck, multi-scale fusion, multi-organ segmentation, image segmentation I. Aug 6, 2024 · Currently, deep learning is developing rapidly in the field of image segmentation, and medical image segmentation is one of the key applications in this field. The proposed target adaptive loss (TAL) allows training a segmentation algorithm over multiple datasets with different labels. Sep 19, 2024 · We rely on two commonly used datasets, Synapse and ACDC, to validate the model. The number of epochs was set to 150 for the Synapse multi-organ segmentation dataset, similar to . Synapse Multi-Organ Segmentation Dataset (Synapse): This dataset includes 3779 axial abdominal clinical CT images from 30 cases, and the voxel spatial resolution is ([0. Synapse is a public multi-organ segmentation dataset. For the experimental evaluation, we replicated the setup detailed in [10]. 1 Datasets and Metrics. Sep 6, 2024 · 4. 12% on the synapse multi-organ CT and ACDC datasets, respectively. Convert them to numpy format, clip the images within [-125, 275], normalize each 3D image to [0, 1], and extract 2D slices from 3D volume for training cases while keeping the 3D volume in h5 format for testing cases. The experimental results on Synapse Multi-organ Segmentation Datasets show that the performance of TransClaw U-Net is better than other network structures. 5% and 89. Dec 19, 2024 · The Synapse multi-organ segmentation dataset (Synapse) comprises 30 cases with a total of 3779 axial abdominal clinical CT images. Index Terms—Multi-organ segmentation, Adaptive spatial ag-gregation, Category balancing I. The Synapse multi-organ segmentation dataset is from the MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge. This improvement is observed to be between 5% and 18%, while the computational cost is reduced by 17% and the amount of parameters is reduced by 19%. And the average Dice-Similarity coefficient (DSC) and average Hausdorff Distance (HD) are used as evaluation metric to Dec 1, 2023 · We evaluate the performance of the proposed model on synapse multiple organ segmentation dataset and automated cardiac diagnostic challenge dataset. 14 GFLOPs. We report both the segmentation performance (DSC, HD95) and model complexity (parameters and FLOPs). Sep 20, 2023 · Synapse multi-organ segmentation dataset:The Synapse multi-organ segmentation dataset includes 30 abdomen CT scans (3779 axial slices in total) that are labeled with 8 organs (including spleen, left kidney, right kidney, gallbladder, liver, spleen, pancreas, aorta, and stomach). following Environement and Library needed to run the code: Python 3; Pytorch. Weconduct extensive experiments to validate the effectiveness of our design. Sep 23, 2023 · 4. We generate our results after data processing, training, and data splits defined in [31]. We evaluate the segmentation results of our approach on four datasets, namely, the synapse multi-organ dataset and the BraTS2021 dataset. The dataset is divided into a training set of 18 Sep 6, 2024 · It integrates multi-scale feature information and inductive bias module for local feature processing, and further improves prediction accuracy through multi-scale information fusion selection. It can be Jun 9, 2024 · GCtx-UNet is evaluated on the Synapse multi-organ abdominal CT dataset, the ACDC cardiac MRI dataset, and several polyp segmentation datasets. 25% on these two datasets, respectively, outperforming other models of similar complexity and size, and achieving state-of-the-art results. Full size table. We perform the ablation studies on the ACDC dataset, D. SAM Adapter and AutoSAM provide a multi-organ segmentation with noise. Synapse for multi-organ 2 The multi-organ segmentation dataset, Synapse, was collected for the 2015 MICCAI challenge and consists of 30 abdominal CT scan cases. 5 ∼ 5. Following 13, the 30 cases were randomly divided by us into 18 training cases and 12 testing cases. Each CT scan consists of 85–198 slices with a resolution of the 512 by 512 pixels. AFFSegNet achieves state-of-the-art results with an average DSC of 90 73%, and AFFSegNet consistently achieves high scores in all organs, demonstrating its ability to generalize to different anatomical structures. 51\%\) improvement in HD May 26, 2021 · Synapse multi-organ segmentation dataset (Synapse) 数据集包括30例3779幅腹部轴位临床CT图像。 在文献[2,34]的基础上,将18个样本划分为训练集,将12个样本划分为测试集。 Jul 14, 2024 · Experiments on 3D slice datasets from ACDC and Synapse demonstrate that SACNet delivers superior segmentation performance in multi-organ segmentation tasks compared to several existing methods. Mar 1, 2024 · Synapse Multi-organ Segmentation Dataset (Synapse). As described in [4, 14], the dataset provides annotations for 8 organs, including aorta, gallbladder, left kidney, right kidney, liver, pancreas, spleen, stomach. coefficient 0. Moreover, the results show that our method outperforms ten state-of-the-art methods and achieves accurate abdominal multi-organ segmentation. Oct 19, 2024 · Synapse multi-organ segmentation dataset (Synapse) consists of 30 cases, with a total of 3779 axial abdominal clinical CT images. 88 DSC and 20. 29% and 92. 54 , 0 . Nov 25, 2023 · The experimental results on the Synapse multi-organ segmentation test dataset are shown in Table 3. The official implementation of SUnet: A multi-organ segmentation network based on multiple attention - XSforAI/SUnet Jul 12, 2021 · The decoding part retains the bottom upsampling structure for better detail segmentation performance. achieved competitive results on abdominal multi-organ and cardiac segmentation datasets. 280 x 280 x 280 mm3 - 500 x 500 Dec 1, 2023 · Synapse Multiple Organ Segmentation Dataset (Synapse): In this experiment, we used 30 labeled abdominal CT scans and 3779 enhanced abdominal images from the MICCAI 2015 Multi-Atlas Abdominal Labeling Challenge. 73%, it slightly trails the best-performing method on three out of eight metrics. The experiments were conducted on Synapse multi-organ segmentation dataset. The results demonstrate that the proposed method achieves Dice similarity coefficient (DSC) metrics of 80. The results for the Synapse multi-organ segmentation dataset are presented in Table II. While SAMed outputs a result with lesser noise, it is also confused by the shape of the organ. 54 ∼ 0. Index Terms: Experiments on 3D slice datasets from ACDC and Synapse demonstrate that SACNet delivers superior segmentation performance in multi-organ segmentation tasks compared to several existing methods. The dataset includes 30 cases with 3779 axial abdominal CT images for the aorta, gallbladder, spleen, left kidney, right kidney, liver, pancreas, and stomach segmentation. Datasets And Metrics Synapse. ipmn eyyf egst nhvdi gmik eau yest msocy qoqag hzcva