Abdominal Multi-organ Segmentation Research Articles

Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images

AbstractMulti‐organ segmentation from abdominal CT scans is crucial for various medical examinations and diagnoses. Despite the remarkable achievements of existing deep‐learning‐based methods, accurately segmenting small organs remains challenging due to their small size and low contrast. This article introduces a novel knowledge‐guided cascaded framework that utilizes two types of knowledge—image intrinsic (anatomy) and clinical expertise (radiology)—to improve the segmentation accuracy of small abdominal organs. Specifically, based on the anatomical similarities in abdominal CT scans, the approach employs entropy‐based registration techniques to map high‐quality segmentation results onto inaccurate results from the first stage, thereby guiding precise localization of small organs. Additionally, inspired by the practice of annotating images from multiple perspectives by radiologists, novel Multi‐View Fusion Convolution (MVFC) operator is developed, which can extract and adaptively fuse features from various directions of CT images to refine segmentation of small organs effectively. Simultaneously, the MVFC operator offers a seamless alternative to conventional convolutions within diverse model architectures. Extensive experiments on the Abdominal Multi‐Organ Segmentation (AMOS) dataset demonstrate the superiority of the method, setting a new benchmark in the segmentation of small organs.

Semi-supervised abdominal multi-organ segmentation by object-redrawing.

Multi-organ segmentation is a critical task in medical imaging, with wide-ranging applications in both clinical practice and research. Accurate delineation of organs from high-resolution 3D medical images, such as CT scans, is essential for radiation therapy planning, enhancing treatment outcomes, and minimizing radiation toxicity risks. Additionally, it plays a pivotal role in quantitative image analysis, supporting various medical research studies. Despite its significance, manual segmentation of multiple organs from 3D images is labor-intensive and prone to low reproducibility due to high interoperator variability. Recent advancements in deep learning have led to several automated segmentation methods, yet many rely heavily on labeled data and human anatomy expertise. In this study, our primary objective is to address the limitations of existing semi-supervised learning (SSL) methods for abdominal multi-organ segmentation. We aim to introduce a novel SSL approach that leverages unlabeled data to enhance the performance of deep neural networks in segmenting abdominal organs. Specifically, we propose a method that incorporates a redrawing network into the segmentation process to correct errors and improve accuracy. Our proposed method comprises three interconnected neural networks: a segmentation network for image segmentation, a teacher network for consistency regularization, and a redrawing network for object redrawing. During training, the segmentation network undergoes two rounds of optimization: basic training and readjustment. We adopt the Mean-Teacher model as our baseline SSL approach, utilizing labeled and unlabeled data. However, recognizing significant errors in abdominal multi-organ segmentation using this method alone, we introduce the redrawing network to generate redrawn images based on CT scans, preserving original anatomical information. Our approach is grounded in the generative process hypothesis, encompassing segmentation, drawing, and assembling stages. Correct segmentation is crucial for generating accurate images. In the basic training phase, the segmentation network is trained using both labeled and unlabeled data, incorporating consistency learning to ensure consistent predictions before and after perturbations. The readjustment phase focuses on reducing segmentation errors by optimizing the segmentation network parameters based on the differences between redrawn and original CT images. We evaluated our method using two publicly available datasets: the beyond the cranial vault (BTCV) segmentation dataset (training: 44, validation: 6) and the abdominal multi-organ segmentation (AMOS) challenge 2022 dataset (training:138, validation:16). Our results were compared with state-of-the-art SSL methods, including MT and dual-task consistency (DTC), using the Dice similarity coefficient (DSC) as an accuracy metric. On both datasets, our proposed SSL method consistently outperformed other methods, including supervised learning, achieving superior segmentation performance for various abdominal organs. These findings demonstrate the effectiveness of our approach, even with a limited number of labeled data. Our novel semi-supervised learning approach for abdominal multi-organ segmentation addresses the challenges associated with this task. By integrating a redrawing network and leveraging unlabeled data, we achieve remarkable improvements in accuracy. Our method demonstrates superior performance compared to existing SSL and supervised learning methods. This approach holds great promise in enhancing the precision and efficiency of multi-organ segmentation in medical imaging applications.

PheoSeg: A 3D transfer learning framework for accurate abdominal CT pheochromocytoma segmentation and surgical grade prediction

Pheochromocytoma is a rare urological disease. Accurate segmentation of pheochromocytoma and its surrounding abdominal organs is crucial for surgical planning and preoperative diagnosis. However, current clinical datasets typically include tumor labels but lack comprehensive multi-organ abdominal labels, and detailed descriptions of the spatial relationships between the tumor and adjacent organs are insufficient. To address the challenge of automatic multi-organ segmentation in the abdomen without comprehensive labels around pheochromocytoma, we propose a novel method for automatic multi-source migration segmentation and preoperative diagnostic grading of pheochromocytoma and its surrounding organs. This method utilizes nnUNet to extract data fingerprint information from the target domain (Clinical Pheochromocytoma dataset of Peking Union Medical College Hospital, PPGL) as prior knowledge, which is then transferred to two source domains (FLARE2022 and AMOS2022). We trained 10 models and incorporated them into a "Model Bank". Additionally, we employed an improved CC-FV method to screen the models in the "Model Bank," identifying the model with the highest mobility (class consistency and feature diversity). This selected model served as a pre-trained model for abdominal multi-organ segmentation and was subsequently adapted for tumor segmentation. To address the issue of limited clinical data, we introduced a data enhancement technique for 3D medical images. The enhanced dataset was used to fine-tune the pre-trained model, resulting in a model capable of segmenting multiple organs and tumors in the abdomen. From the segmentation results, we extracted the spatial relationships between the tumor and surrounding abdominal organs and constructed a radiomic model to predict and analyze the surgical grading. Experimental results demonstrate that our proposed method achieves a Dice coefficient that is, on average, 10.18 % higher on FLARE2022 and AMOS2022 datasets compared to segmentation frameworks such as Swin-Unet, UNETR, CoTr, nnformer, and nnUNetv2. For the PPGL dataset, the Dice coefficient for pheochromocytoma and surrounding organs was 87.27 %. In summary, our method effectively realizes efficient and automatic segmentation of pheochromocytoma and surrounding organs, as well as preoperative diagnostic classification.

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique.

Abdominal multi-organ segmentation is one of the most important topics in the field of medical image analysis, and it plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. In this study, an efficient multi-organ segmentation method called Swin-PSAxialNet based on the nnU-Net architecture is proposed. It was designed specifically for the precise segmentation of 11 abdominal organs in CT images. The proposed network has made the following improvements compared to nnU-Net. Firstly, Space-to-depth (SPD) modules and parameter-shared axial attention (PSAA) feature extraction blocks were introduced, enhancing the capability of 3D image feature extraction. Secondly, a multi-scale image fusion approach was employed to capture detailed information and spatial features, improving the capability of extracting subtle features and edge features. Lastly, a parameter-sharing method was introduced to reduce the model's computational cost and training speed. The proposed network achieves an average Dice coefficient of 0.93342 for the segmentation task involving 11 organs. Experimental results indicate the notable superiority of Swin-PSAxialNet over previous mainstream segmentation methods. The method shows excellent accuracy and low computational costs in segmenting major abdominal organs.

I[formula omitted]U-Net: A dual-path U-Net with rich information interaction for medical image segmentation

Although the U-shape networks have achieved remarkable performances in many medical image segmentation tasks, they rarely model the sequential relationship of hierarchical layers. This weakness makes it difficult for the current layer to effectively utilize the historical information of the previous layer, leading to unsatisfactory segmentation results for lesions with blurred boundaries and irregular shapes. To solve this problem, we propose a novel dual-path U-Net, dubbed I2U-Net. The newly proposed network encourages historical information re-usage and re-exploration through rich information interaction among the dual paths, allowing deep layers to learn more comprehensive features that contain both low-level detail description and high-level semantic abstraction. Specifically, we introduce a multi-functional information interaction module (MFII), which can model cross-path, cross-layer, and cross-path-and-layer information interactions via a unified design, making the proposed I2U-Net behave similarly to an unfolded RNN and enjoying its advantage of modeling time sequence information. Besides, to further selectively and sensitively integrate the information extracted by the encoder of the dual paths, we propose a holistic information fusion and augmentation module (HIFA), which can efficiently bridge the encoder and the decoder. Extensive experiments on four challenging tasks, including skin lesion, polyp, brain tumor, and abdominal multi-organ segmentation, consistently show that the proposed I2U-Net has superior performance and generalization ability over other state-of-the-art methods. The code is available at https://github.com/duweidai/I2U-Net.

Efficient multiscale spatial attention 3D abdominal multiorgan segmentation model

AbstractAccurate and efficient 3D medical image segmentation models are critical for clinical applications. However, many models which performed well in recent years have consumed significant computational cost, especially those based on self‐attention. For more efficient and accurate segmentation of medical 3D images, we develop an efficient multiscale spatial attention model for 3D abdominal multiorgan segmentation. We use 1D and 2D kernels in the encoder and decoder modules to simulate 3D kernels to reduce the number of parameters and computational cost. Furthermore, we use the lightweight spatial attention and mixed pooling module to capture cross‐dimensional information, multiscale features, and long‐range contextual information. We achieved a Dice Similarity Coefficient (DSC) of 89.86 and a Normalized Surface Dice (NSD) of 78.2 on the FLARE dataset with only 1183 M GPU memory usage, 9 M parameters, and 193G floating point operations(FLOPs).

Abdominal multi-organ segmentation using multi-scale and context-aware neural networks

Recent advancements in AI have significantly enhanced smart diagnostic methods, bringing us closer to achieving end-to-end diagnosis. Ultrasound image segmentation plays a crucial role in this diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we consider two inherent features of ultrasound images: (1) different organs and tissues vary in spatial sizes, and (2) the anatomical structures inside the human body form a relatively constant spatial relationship. Based on those two ideas, we proposed two segmentation models combining multi-scale convolution neural network backbones and a spatial context feature extractor. We discuss two backbone structures to extract anatomical structures of different scales: the Feature Pyramid Network (FPN) backbone and the Trident Network backbone. Moreover, we show how Spatial Recurrent Neural Network (SRNN) is implemented to extract the spatial context features in abdominal ultrasound images. Our proposed model has achieved dice coefficient score of 0.919 and 0.931, respectively.

Multi-phase CT synthesis-assisted segmentation of abdominal organs

To propose a method for abdominal multi-organ segmentation assisted by multi-phase CT synthesis. Multi-phase CT synthesis for synthesizing high-quality CT images was used to increase the information details for image segmentation. A transformer block was introduced to help to capture long-range semantic information in cooperation with perceptual loss to minimize the differences between the real image and synthesized image. The model was trained using multi-phase CT dataset of 526 total cases from Nanfang Hospital. The mean maximum absolute error (MAE) of the synthesized non-contrast CT, venous phase contrast- enhanced CT (CECT), and delay phase CECT images from arterial phase CECT was 19.192±3.381, 20.140±2.676 and 22.538±2.874, respectively, which were better than those of images synthesized using other methods. Validation of the multi-phase CT synthesis-assisted abdominal multi-organ segmentation method showed an average dice coefficient of 0.847 for the internal validation set and 0.823 for the external validation set. The propose method is capable of synthesizing high-quality multi-phase CT images to effectively reduce the errors in registration between different phase CT images and improve the performance for segmentation of 13 abdominal organs.

Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding.

Automatic parsing of human anatomies at the instance-level from 3D computed tomography (CT) is a prerequisite step for many clinical applications. The presence of pathologies, broken structures or limited field-of-view (FOV) can all make anatomy parsing algorithms vulnerable. In this work, we explore how to leverage and implement the successful detection-then-segmentation paradigm for 3D medical data, and propose a steerable, robust, and efficient computing framework for detection, identification, and segmentation of anatomies in CT scans. Considering the complicated shapes, sizes, and orientations of anatomies, without loss of generality, we present a nine degrees of freedom (9-DoF) pose estimation solution in full 3D space using a novel single-stage, non-hierarchical representation. Our whole framework is executed in a steerable manner where any anatomy of interest can be directly retrieved to further boost inference efficiency. We have validated our method on three medical imaging parsing tasks: ribs, spine, and abdominal organs. For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs. Extensive experiments on 9-DoF box detection and rib instance segmentation demonstrate the high efficiency and effectiveness of our framework (with the identification rate of 97.0% and the segmentation Dice score of 90.9%), compared favorably against several strong baselines (e.g., CenterNet, FCOS, and nnU-Net). For spine parsing and abdominal multi-organ segmentation, our method achieves competitive results on par with stateof-the-art methods on the public CTSpine1K dataset and FLARE22 competition, respectively. Our annotations, code, and models are available at: Med-Query.

Artificial Intelligence Generated Data Augmentation for Abdominal Multi-Organ Segmentation

Artificial Intelligence Generated Data Augmentation for Abdominal Multi-Organ Segmentation

SEF-UNet: advancing abdominal multi-organ segmentation with SEFormer and depthwise cascaded upsampling.

The abdomen houses multiple vital organs, which are associated with various diseases posing significant risks to human health. Early detection of abdominal organ conditions allows for timely intervention and treatment, preventing deterioration of patients' health. Segmenting abdominal organs aids physicians in more accurately diagnosing organ lesions. However, the anatomical structures of abdominal organs are relatively complex, with organs overlapping each other, sharing similar features, thereby presenting challenges for segmentation tasks. In real medical scenarios, models must demonstrate real-time and low-latency features, necessitating an improvement in segmentation accuracy while minimizing the number of parameters. Researchers have developed various methods for abdominal organ segmentation, ranging from convolutional neural networks (CNNs) to Transformers. However, these methods often encounter difficulties in accurately identifying organ segmentation boundaries. MetaFormer abstracts the framework of Transformers, excluding the multi-head Self-Attention, offering a new perspective for solving computer vision problems and overcoming the limitations of Vision Transformers and CNN backbone networks. To further enhance segmentation effectiveness, we propose a U-shaped network, integrating SEFormer and depthwise cascaded upsampling (dCUP) as the encoder and decoder, respectively, into the UNet structure, named SEF-UNet. SEFormer combines Squeeze-and-Excitation modules with depthwise separable convolutions, instantiating the MetaFormer framework, enhancing the capture of local details and texture information, thereby improving edge segmentation accuracy. dCUP further integrates shallow and deep information layers during the upsampling process. Our model significantly improves segmentation accuracy while reducing the parameter count and exhibits superior performance in segmenting organ edges that overlap each other, thereby offering potential deployment in real medical scenarios.

D-SAT: dual semantic aggregation transformer with dual attention for medical image segmentation

Objective. Medical image segmentation is significantly essential to assist clinicians in facilitating a quick and accurate diagnoses. However, most of the existing methods are still challenged by the loss of semantic information, blurred boundaries and the huge semantic gap between the encoder and decoder. Approach. To tackle these issues, a dual semantic aggregation transformer with dual attention is proposed for medical image segmentation. Firstly, the dual-semantic feature aggregation module is designed to build a bridge between convolutional neural network (CNN) and Transformer, effectively aggregating CNN’s local feature detail ability and Transformer’s long-range modeling ability to mitigate semantic information loss. Thereafter, the strip spatial attention mechanism is put forward to alleviate the blurred boundaries during encoding by constructing pixel-level feature relations across CSWin Transformer blocks from different spatial dimensions. Finally, a feature distribution gated attention module is constructed in the skip connection between the encoder and decoder to decrease the large semantic gap by filtering out the noise in low-level semantic information when fusing low-level and high-level semantic features during decoding. Main results. Comprehensive experiments conducted on abdominal multi-organ segmentation, cardiac diagnosis, polyp segmentation and skin lesion segmentation serve to validate the generalization and effectiveness of the proposed dual semantic aggregation transformer with dual attention (D-SAT). The superiority of D-SAT over current state-of-the-art methods is substantiated by both subjective and objective evaluations, revealing its remarkable performance in terms of segmentation accuracy and quality. Significance. The proposed method subtly preserves the local feature details and global context information in medical image segmentation, providing valuable support to improve diagnostic efficiency for clinicians and early disease control for patients. Code is available at https://github.com/Dxkm/D-SAT.

DSKCA-UNet: Dynamic selective kernel channel attention for medical image segmentation.

U-Net has attained immense popularity owing to its performance in medical image segmentation. However, it cannot be modeled explicitly over remote dependencies. By contrast, the transformer can effectively capture remote dependencies by leveraging the self-attention (SA) of the encoder. Although SA, an important characteristic of the transformer, can find correlations between them based on the original data, secondary computational complexity might retard the processing rate of high-dimensional data (such as medical images). Furthermore, SA is limited because the correlation between samples is overlooked; thus, there is considerable scope for improvement. To this end, based on Swin-UNet, we introduce a dynamic selective attention mechanism for the convolution kernels. The weight of each convolution kernel is calculated to fuse the results dynamically. This attention mechanism permits each neuron to adaptively modify its receptive field size in response to multiscale input information. A local cross-channel interaction strategy without dimensionality reduction was introduced, which effectively eliminated the influence of downscaling on learning channel attention. Through suitable cross-channel interactions, model complexity can be significantly reduced while maintaining its performance. Subsequently, the global interaction between the encoder features is used to extract more fine-grained features. Simultaneously, the mixed loss function of the weighted cross-entropy loss and Dice loss is used to alleviate category imbalances and achieve better results when the sample number is unbalanced. We evaluated our proposed method on abdominal multiorgan segmentation and cardiac segmentation datasets, achieving Dice similarity coefficient and 95% Hausdorff distance metrics of 80.30 and 14.55%, respectively, on the Synapse dataset and Dice similarity coefficient metrics of 90.80 on the ACDC dataset. The experimental results show that our proposed method has good generalization ability and robustness, and it is a powerful tool for medical image segmentation.

Dynamic weight HiLo attention network for medical image multiple organ segmentation

AbstractIn recent years, there has been a surge of research in the field of medical image segmentation using hybrid CNN‐Transformer network architectures. Most of these studies leverage the attention mechanism of ViT to overcome the limitations of CNN architectures in capturing long‐range dependencies. However, these hybrid model approaches also have some potential drawbacks. First, due to the heavy reliance of the Transformer's attention mechanism on global information, it can lead to a significant increase in computational cost when dealing with high‐resolution input images. Furthermore, the convolutional and attention mechanisms in hybrid models have different interpretability in information extraction and decision‐making, which poses a challenge for the interpretability of the convolutional part. Our proposed model, DWHA, addresses these limitations and outperforms state‐of‐the‐art models in a range of medical image segmentation tasks, such as abdominal multiorgan segmentation, automatic cardiac diagnosis, neurostructure segmentation, and skin lesion segmentation, achieving significantly superior performance. Specifically, on the abdominal multiorgan segmentation dataset, DWHA outperformed the previous state‐of‐the‐art baseline by 0.57%; on the neurostructure segmentation dataset, it achieved an improvement of 1.17%; and on the skin lesion segmentation dataset, it achieved an improvement of 0.91%. These significant improvements suggest that DWHA may become the preferred model in the field of medical image segmentation.

A novel one-to-multiple unsupervised domain adaptation framework for abdominal organ segmentation.

Abdominal multi-organ segmentation in multi-sequence magnetic resonance images (MRI) is of great significance in many clinical scenarios, e.g., MRI-oriented pre-operative treatment planning. Labeling multiple organs on a single MR sequence is a time-consuming and labor-intensive task, let alone manual labeling on multiple MR sequences. Training a model by one sequence and generalizing it to other domains is one way to reduce the burden of manual annotation, but the existence of domain gap often leads to poor generalization performance of such methods. Image translation-based unsupervised domain adaptation (UDA) is a common way to address this domain gap issue. However, existing methods focus less on keeping anatomical consistency and are limited by one-to-one domain adaptation, leading to low efficiency for adapting a model to multiple target domains. This work proposes a unified framework called OMUDA for one-to-multiple unsupervised domain-adaptive segmentation, where disentanglement between content and style is used to efficiently translate a source domain image into multiple target domains. Moreover, generator refactoring and style constraint are conducted in OMUDA for better maintaining cross-modality structural consistency and reducing domain aliasing. The average Dice Similarity Coefficients (DSCs) of OMUDA for multiple sequences and organs on the in-house test set, the AMOS22 dataset and the CHAOS dataset are 85.51%, 82.66% and 91.38%, respectively, which are slightly lower than those of CycleGAN(85.66% and 83.40%) in the first two data sets and slightly higher than CycleGAN(91.36%) in the last dataset. But compared with CycleGAN, OMUDA reduces floating-point calculations by about 87 percent in the training phase and about 30 percent in the inference stage respectively. The quantitative results in both segmentation performance and training efficiency demonstrate the usability of OMUDA in some practical scenes, such as the initial phase of product development.

Partial Label Multi-organ Segmentation based on Local Feature Enhancement.

The automatic segmentation of abdominal organs from CT images is essential for surgical planning of abdominal diseases. However, each medical institution only annotates some organs according to its own clinical practice. This brings the partial annotation problem to multi-center abdominal multi-organ segmentation. To address this issue, we introduce a 3D local feature enhanced multi-head segmentation network for multi-organ segmentation of abdominal regions in multiple partially labeled datasets. More specifically, our proposed architecture consists of two branches, the global branch with 3D Transformer and U-Net fusion named 3D TransUNet as the backbone, and the local 3D U-Net branch that provides additional abdominal organ structure information to the global branch to generate more accurate segmentation results. We evaluate our method on four publicly available CT datasets with four different partial label. Our experiments show that the proposed approach provides better accuracy and robustness, with 93.01% average Dice-score-coefficient (DSC) and 3.489 mm Hausdorff Distance (HD) outperforming three existing state-of-the-art methods.

Mind the Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation

Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in this campaign relies upon aligning the distributions of source and target domain. One common attempt is to enforce the global alignment between two domains, which, however, ignores the fatal local-imbalance domain gap problem, i.e., some local features with larger domain gap are harder to transfer. Recently, some methods conduct alignment focusing on local regions to improve the efficiency of model learning. While this operation may cause a deficiency of critical information from contexts. To tackle this limitation, we propose a novel strategy to alleviate the domain gap imbalance considering the characteristics of medical images, namely Global-Local Union Alignment. Specifically, a feature-disentanglement style-transfer module first synthesizes the target-like source images to reduce the global domain gap. Then, a local feature mask is integrated to reduce the 'inter-gap' for local features by prioritizing those discriminative features with larger domain gap. This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency. We conduct a series of experiments with two cross-modality adaptation tasks, i,e. cardiac substructure and abdominal multi-organ segmentation. Experimental results indicate that our method achieves state-of-the-art performance in both tasks.

Contour-aware network with class-wise convolutions for 3D abdominal multi-organ segmentation.

Accurate delineation of multiple organs is a critical process for various medical procedures, which could be operator-dependent and time-consuming. Existing organ segmentation methods, which were mainly inspired by natural image analysis techniques, might not fully exploit the traits of the multi-organ segmentation task and could not accurately segment the organs with various shapes and sizes simultaneously. In this work, the characteristics of multi-organ segmentation are considered: the global count, position and scale of organs are generally predictable, while their local shape and appearance are volatile. Thus, we supplement the region segmentation backbone with a contour localization task to increase the certainty along delicate boundaries. Meantime, each organ has exclusive anatomical traits, which motivates us to deal with class variability with class-wise convolutions to highlight organ-specific features and suppress irrelevant responses at different field-of-views. To validate our method with adequate amounts of patients and organs, we constructed a multi-center dataset, which contains 110 3D CT scans with 24,528 axial slices, and provided voxel-level manual segmentations of 14 abdominal organs, which adds up to 1,532 3D structures in total. Extensive ablation and visualization studies on it validate the effectiveness of the proposed method. Quantitative analysis shows that we achieve state-of-the-art performance for most abdominal organs, and obtain 3.63 mm 95% Hausdorff Distance and 83.32% Dice Similarity Coefficient on an average.

CI-UNet: Application of Segmentation of Medical Images of the Human Torso

The study of human torso medical image segmentation is significant for computer-aided diagnosis of human examination, disease tracking, and disease prevention and treatment. In this paper, two application tasks are designed for torso medical images: the abdominal multi-organ segmentation task and the spine segmentation task. For this reason, this paper proposes a net-work model CI-UNet improve the accuracy of edge segmentation. CI-UNet is a U-shaped network structure consisting of encoding and decoding networks. Firstly, it replaces UNet’s double convolutional backbone network with a VGG16 network loaded with Transfer Learning. It feeds image information from two adjacent layers in the VGG16 network into the decoding grid via information aggregation blocks. Secondly, Polarized Self-Attention is added at the decoding network and the hopping connection, which allows the network to focus on the compelling features of the image. Finally, the image information is decoded by convolution and Up-sampling several times to obtain the segmentation results. CI-UNet was tested in the abdominal multi-organ segmentation task using the Chaos (Combined CT-MR Healthy Abdominal Organ Segmentation) open challenge dataset and compared with UNet, Attention UNet, PSPNet, DeepLabv3+ prediction networks, and dedicated network for MRI images. The experimental results showed that the average intersegmental union (mIoU) and average pixel accuracy (mPA) of organ segmentation were 82.33% and 90.10%, respectively, higher than the above comparison network. Meanwhile, we used CI-UNet for the spine dataset of the Guizhou branch of Beijing Jishuitan Hospital. The average intersegmental union (mIoU) and average pixel accuracy (mPA) of organ segmentation were 87.97% and 93.48%, respectively, which were approved by the physicians for both tasks.

Unsupervised Cross-modality Adaptation via Dual Structural-Oriented Guidance for 3D Medical Image Segmentation.

Deep convolutional neural networks (CNNs) have achieved impressive performance in medical image segmentation; however, their performance could degrade significantly when being deployed to unseen data with heterogeneous characteristics. Unsupervised domain adaptation (UDA) is a promising solution to tackle this problem. In this work, we present a novel UDA method, named dual adaptation-guiding network (DAG-Net), which incorporates two highly effective and complementary structural-oriented guidance in training to collaboratively adapt a segmentation model from a labelled source domain to an unlabeled target domain. Specifically, our DAG-Net consists of two core modules: 1) Fourier-based contrastive style augmentation (FCSA) which implicitly guides the segmentation network to focus on learning modality-insensitive and structural-relevant features, and 2) residual space alignment (RSA) which provides explicit guidance to enhance the geometric continuity of the prediction in the target modality based on a 3D prior of inter-slice correlation. We have extensively evaluated our method with cardiac substructure and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our DAG-Net greatly outperforms the state-of-the-art UDA approaches for 3D medical image segmentation on unlabeled target images.