Framework For Image Segmentation Research Articles

Cross-level collaborative context-aware framework for medical image segmentation

Efficient and accurate medical image segmentation is necessary for pathological evaluation and disease diagnosis in clinical practice. In recent years, the U-shaped encoder–decoder structure has achieved good performance in various medical image segmentation tasks. It is a neural network model similar to the letter U, which can gradually reduce the spatial dimension of the feature map to capture high-level semantic information in the feature encoding stage, and can make up for the lack of target detail information in the feature decoding stage to decouple the final segmentation results. However, the U-shaped encoder–decoder structure still suffers from semantic asymmetry and global semantic dilution problems, which are exacerbated during decoding, due to the limitation of the framework’s fixation. In this paper, we propose a cross-level collaborative context-aware framework (C3-Net) to address the aforementioned issues in medical image segmentation. The main contributions of this research include: (i) To address the inherent problems in the U-shaped structure, we propose the C3-Net to explore the differences between cross-level contextual information effectively; (ii) To achieve detailed information preservation and semantic information enhancement, we present a Channel Scale-aware Context Enhancement (CSCE) module to enhance low-level contextual features from the global and local scales of the channel dimension; (iii) A Spatial Pyramid Context Alignment (SPCA) module is designed to extract and align pyramid features, thus obtaining accurate global contextual features at the network’s top; (iv) We propose a Cross-Level Collaborative Context Refinement (CCCR) module to enable efficient collaboration between cross-level contextual features to simultaneously achieve semantic alignment and global semantic enhancement. The outcomes of implementing the proposed method on three publicly available datasets (Synapse, ACDC, and GlaS) and one private dataset (VMICH) show that the proposed method outperforms all competing approaches on four datasets. Our C3-Net achieves new state-of-the-art performance on four image segmentation tasks with Dice scores of 85.26%, 92.10%, 87.20%, and 87.61%, respectively.

Generalized framework for image and video object segmentation using affinity learning and message passing GNNS

Despite significant amount of work reported in the computer vision literature, segmenting images or videos based on multiple cues such as objectness, texture and motion, is still a challenge. This is particularly true when the number of objects to be segmented is not known or there are objects that are not classified in the training data (unknown objects). A possible remedy to this problem is to utilize graph-based clustering techniques such as Correlation Clustering. It is known that using long range affinities (Lifted multicut), makes correlation clustering more accurate than using only adjacent affinities (Multicut). However, the former is computationally expensive and hard to use. In this paper, we introduce a new framework to perform image/motion segmentation using an affinity learning module and a Message Passing Graph Neural Network (MPGNN). The affinity learning module uses a permutation invariant affinity representation to overcome the multi-object problem. The paper shows, both theoretically and empirically, that the proposed MPGNN aggregates higher order information and thereby converts the Lifted Multicut Problem (LMP) to a Multicut Problem (MP), which is easier and faster to solve. Importantly, the proposed method can be generalized to deal with different clustering problems with the same MPGNN architecture. For instance, our method produces competitive results for single image segmentation (on BSDS dataset) as well as unsupervised video object segmentation (on DAVIS17 dataset), by only changing the feature extraction part. In addition, using an ablation study on the proposed MPGNN architecture, we show that the way we update the parameterized affinities directly contributes to the accuracy of the results.

Dynamic contrastive learning guided by class confidence and confusion degree for medical image segmentation

This work proposes an intra-Class-confidence and inter-Class-confusion guided Dynamic Contrastive (CCDC) learning framework for medical image segmentation. A core contribution is to dynamically select the most expressive pixels to build positive and negative pairs for contrastive learning at different training phases. For the positive pairs, dynamically adaptive sampling strategies are introduced for sampling different sets of pixels based on their hardness (namely the easiest, easy, and hard pixels). For the negative pairs, to efficiently learn from the classes with high confusion degree w.r.t a query class (i.e., a class containing the query pixels), a new hard class mining strategy is presented. Furthermore, pixel-level representations are extended to the neighbourhood region to leverage the spatial consistency of adjacent pixels. Extensive experiments on the three public datasets demonstrate that the proposed method significantly surpasses the state-of-the-art.

Synergistic Label-Stability Learning for Semi-supervised Left Atrium Segmentation.

Recent semi-supervised learning approaches appealingly advance medical image segmentation for their effectiveness in alleviating the need for a large amount of expert-demanding annotations. However, most of them have two limitations: (i) neglect of the intra-class variation caused by different patients and scanning protocols, which makes the pixel-level label propagation difficult; (ii) non-selective stability learning (a.k.a., consistency regularization), resulting in distraction by the redundant easy regions. To address these, in this work, we propose a novel synergistic label-stability learning (SLSL) framework for semi-supervised medical image segmentation. Specifically, our method is built upon the teacher-student framework. Then, the label learning process includes the typical pseudo label learning that reinforces confirmation of well-classified easy regions and the cyclic real label learning that takes advantage of real labels and class prototypes to regularize the distribution of intra-class features from unlabeled data to facilitate label propagation. In addition, the difficulty-selective stability learning aims to regularize the perturbed stability only at the high-entropy (can be regarded as difficult) regions, rather than being distracted by the less-informative easy regions. Extensive experiments on left atrium segmentation from MRI show that our method can effectively exploit the unlabeled data and outperform other semi-supervised medical image segmentation methods.Clinical relevance- The proposed method can help develop a high-performance automatic left atrium segmentation model for treating atrial fibrillation under limited expert-demanding annotation budgets.

Expert- and Nonexpert-friendly Framework for Deep Learning Image Segmentation Demonstrates Successes Across Applications in vEM, CryoEM, MicroCT and Fluorescence Microscopy.

Expert- and Nonexpert-friendly Framework for Deep Learning Image Segmentation Demonstrates Successes Across Applications in vEM, CryoEM, MicroCT and Fluorescence Microscopy.

A robust and consistent stack generalized ensemble-learning framework for image segmentation

In the present study, we aim to propose an effective and robust ensemble-learning approach with stacked generalization for image segmentation. Initially, the input images are processed for feature extraction and edge detection using the Gabor filter and the Canny algorithms, respectively; our main goal is to determine the most feature descriptions. Subsequently, we applied the stacking generalization technique, which is generally built with two main learning levels. The first level is composed of two algorithms that give good results in the literature, namely: LightGBM (Light Gradient Boosting Machine) and SVM (support vector machine). The second level is the meta-model in which we use a predictor model that takes the base-level predictions to improve the accuracy of the final prediction. In the stacked generalization process, we use the Extreme Gradient Boosting (XGBoost); it takes as input the sub-models’ outputs to better classify each pixel of the image to give the final prediction. Today, several research works exist in the literature using different machine learning algorithms; in fact, instead of trying to find a single efficient and optimal learner, ensemble-based techniques take the advantage of each basic model; they integrate their outputs to obtain a more consistent and reliable learner. The result obtained from the models of individuals and our proposed approach is compared using a set of evaluation measures for image quality such as IoU, DSC, CC, SSIM, SAM, and UQI. The evaluation and a comparison of the results obtained showed more consistent predictions for the proposed model. Thus, we have made a comparison with some recent deep learning-based unsupervised segmentation methods. The evaluation and a comparison of the results obtained showed more coherent predictions for our stacked generalization in terms of precision, robustness, and consistency.

Model-driven self-aware self-training framework for label noise-tolerant medical image segmentation

The label noise-tolerant deep learning methods have been exploited extensively in pattern classification tasks, but still less studied in image segmentation tasks. Especially, the segmentation annotations are prone to suffering from inter-rater variability and subjectivity in medical image segmentation tasks. The study of the label noise-tolerant learning with corrupted label is an important and active topic. In this paper, we propose a model-driven self-aware self-training segmentation framework for label noise-tolerant learning. “Model-driven” means that we propose a model state estimation method to discover the early learning time from noisy labels in different segmentation tasks. Second, we propose a pseudo label generation method to modify the training process with noisy annotations. The pseudo label generation is based on the prototypical feature learning without extra parameters. This method exploits the similarity and consistency from the pixel-wise feature to the global prototypes, which can effectively remedy the incorrect labels, thus called “self-aware”. Further, we introduce the self-training with the generated pseudo label to boost robustness against noisy labels. Extensive experiments on three public datasets demonstrate that our method outperforms other popular anti-noise segmentation methods, and has shown effectiveness and stability when facing with different kinds of label noises. Our method improves the direct training by 5.80% Dsc on PROMISE12 dataset under annotations with 75% noise ratio.

Unsupervised Domain Adaptation for Medical Image Segmentation by Selective Entropy Constraints and Adaptive Semantic Alignment

Generalizing a deep learning model to new domains is crucial for computer-aided medical diagnosis systems. Most existing unsupervised domain adaptation methods have made significant progress in reducing the domain distribution gap through adversarial training. However, these methods may still produce overconfident but erroneous results on unseen target images. This paper proposes a new unsupervised domain adaptation framework for cross-modality medical image segmentation. Specifically, We first introduce two data augmentation approaches to generate two sets of semantics-preserving augmented images. Based on the model's predictive consistency on these two sets of augmented images, we identify reliable and unreliable pixels. We then perform a selective entropy constraint: we minimize the entropy of reliable pixels to increase their confidence while maximizing the entropy of unreliable pixels to reduce their confidence. Based on the identified reliable and unreliable pixels, we further propose an adaptive semantic alignment module which performs class-level distribution adaptation by minimizing the distance between same class prototypes between domains, where unreliable pixels are removed to derive more accurate prototypes. We have conducted extensive experiments on the cross-modality cardiac structure segmentation task. The experimental results show that the proposed method significantly outperforms the state-of-the-art comparison algorithms. Our code and data are available at https://github.com/fengweie/SE_ASA.

TC-Net: A joint learning framework based on CNN and vision transformer for multi-lesion medical images segmentation

BackgroundWith the rapid advancement of medical imaging technology, the demand for accurate segmentation of medical images is increasing. However, most existing methods are unable to capture locality and long-range dependency information in integrated ways for medical images. MethodIn this paper, we propose an elegant segmentation framework for medical images named TC-Net, which can utilize both the locality-aware and long-range dependencies in the medical images. As for the locality-aware perspective, we employ a CNN-based encoder and decoder structure. The CNN branch uses the locality of convolution operations to dig out local information in medical images. As for the long-range dependencies, we construct a Transformer branch to focus on the global context. Additionally, we proposed a locality-aware and long-range dependency concatenation strategy (LLCS) to aggregate the feature maps obtained from the two subbranches. Finally, we present a dynamic cyclical focal loss (DCFL) to address the class imbalance problem in multi-lesion segmentation. ResultsComprehensive experiments were conducted on lesion segmentation tasks using two fundus image databases and a skin image database. The TC-Net achieves scores of 0.6985 and 0.5171 in the metric of mean pixel accuracy on the IDRiD and DDR databases, respectively. Moreover, on the skin image database, the TC-Net reached mean pixel accuracy of 0.8886. The experiment results demonstrate that the proposed method achieves better performance than other deep learning segmentation schemes. Furthermore, the proposed DCFL achieves higher performance than other loss functions in multi-lesion segmentation. SignificanceThe proposed TC-Net is a promising new framework for multi-lesion medical image segmentation and many other challenging image segmentation tasks. © 2001 Elsevier Science. All rights reserved.

A medical unsupervised domain adaptation framework based on Fourier transform image translation and multi-model ensemble self-training strategy.

Well-established segmentation models will suffer performance degradation when deployed on data with heterogeneous features, especially in the field of medical image analysis. Although researchers have proposed many approaches to address this problem in recent years, most of them are feature-adaptation-based adversarial networks, the problems such as training instability often arise in adversarial training. To ameliorate this challenge and improve the robustness of processing data with different distributions, we propose a novel unsupervised domain adaptation framework for cross-domain medical image segmentation. In our proposed approach, Fourier transform guided images translation and multi-model ensemble self-training are integrated into a unified framework. First, after Fourier transform, the amplitude spectrum of source image is replaced with that of target image, and reconstructed by the inverse Fourier transform. Second, we augment target dataset with the synthetic cross-domain images, performing supervised learning using the original source set labels while implementing regularization by entropy minimization on predictions of unlabeled target data. We employ several segmentation networks with different hyperparameters simultaneously, pseudo-labels are generated by averaging their outputs and comparing to confidence threshold, and gradually optimize the quality of pseudo-labels through multiple rounds self-training. We employed our framework to two liver CT datasets for bidirectional adaptation experiments. In both experiments, compared to the segmentation network without domain alignment, dice similarity coefficient (DSC) increased by nearly 34% and average symmetric surface distance (ASSD) decreased by about 10. The DSC values were also improved by 10.8% and 6.7%, respectively, compared to the existing model. We propose a Fourier transform-based UDA framework, the experimental results and comparisons demonstrate that the proposed method can effectively diminish the performance degradation caused by domain shift and performs best on the cross-domain segmentation tasks. Our proposed multi-model ensemble training strategy can also improve the robustness of the segmentation system.

Synchronous Medical Image Augmentation framework for deep learning-based image segmentation.

Various deep learning (DL) models are widely applied in medical image analysis, and their performance depends on the scale and diversity of available training data. However, medical images often suffer from difficulty in data acquisition, imbalance in sample categories, and high cost of labeling. In addition, most image augmentation approaches mainly focus on image synthesis only for classification tasks, and rarely consider the synthetic image-label pairs for image segmentation tasks. In this paper, we focus on the medical image augmentation for DL-based image segmentation and the synchronization between augmented image samples and their labels. We design a Synchronous Medical Image Augmentation (SMIA) framework, which includes two modules based on stochastic transformation and synthesis, and provides diverse and annotated training sets for DL models. In the transform-based SMIA module, for each medical image sample and its tissue segments, a subset of SMIA factors with a random number of factors and stochastic parameter values are selected to simultaneously generate augmented samples and the paired tissue segments. In the synthesis-based SMIA module, we randomly replace the original tissues with the augmented tissues using an equivalent replacement method to synthesize new medical images, which can well maintain the original medical implications. DL-based image segmentation experiments on bone marrow smear and dermoscopic images demonstrate that the proposed SMIA framework can generate category-balanced and diverse training data, and have a positive impact on the performance of the models.

A Tumor MRI Image Segmentation Framework Based on Class-Correlation Pattern Aggregation in Medical Decision-Making System

Medical image analysis methods have been applied to clinical scenarios of tumor diagnosis and treatment. Many studies have attempted to optimize the effectiveness of tumor MRI image segmentation by deep learning, but they do not consider the optimization of local details and the interaction of global semantic information. Second, although medical image pattern recognition can learn representative semantic features, it is challenging to ignore useless features in order to learn generalizable embeddings. Thus, a tumor-assisted segmentation method is proposed to detect tumor lesion regions and boundaries with complex shapes. Specifically, we introduce a denoising convolutional autoencoder (DCAE) for MRI image noise reduction. Furthermore, we design a novel tumor MRI image segmentation framework (NFSR-U-Net) based on class-correlation pattern aggregation, which first aggregates class-correlation patterns in MRI images to form a class-correlational representation. Then the relationship of similar class features is identified to closely correlate the dense representations of local features for classification, which is conducive to identifying image data with high heterogeneity. Meanwhile, the model uses a spatial attention mechanism and residual structure to extract effective information of the spatial dimension and enhance statistical information in MRI images, which bridges the semantic gap in skip connections. In the study, over 4000 MRI images from the Monash University Research Center for Artificial Intelligence are analyzed. The results show that the method achieves segmentation accuracy of up to 96% for tumor MRI images with low resource consumption.

UNET MOBILENETV2: MEDICAL IMAGE SEGMENTATION USING DEEP NEURAL NETWORK (DNN)

In this paper, the framework of polyp image segmentation is developed using a Deep neural network (DNN). Here Unet Mobile NetV2 is considered to evaluate the performance of the image from the CVC-612 dataset for the segmentation method. The proposed model outperformed earlier results. To compare our results two parameters, normally Dice co-efficient and Intersection over Union (IoU) are considered. The proposed model may be used for accurate computer-aided polyp detection and segmentation during colonoscopy examinations to find out abnormal tissue and thereby decrease the chances of polyps growing into cancer. MobileNetV2 significantly outperforms U-Net and MobileNetV2, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 89.71%, and an IoU of 81.64%.

Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation

Learning discriminative representations of unlabelled data is a challenging task. Contrastive self-supervised learning provides a framework to learn meaningful representations using learned notions of similarity measures from simple pretext tasks. In this work, we propose a simple and efficient framework for self-supervised image segmentation using contrastive learning on image patches, without using explicit pretext tasks or any further labeled fine-tuning. A fully convolutional neural network (FCNN) is trained in a self-supervised manner to discern features in the input images and obtain confidence maps which capture the network's belief about the objects belonging to the same class. Positive- and negative- patches are sampled based on the average entropy in the confidence maps for contrastive learning. Convergence is assumed when the information separation between the positive patches is small, and the positive-negative pairs is large. The proposed model only consists of a simple FCNN with 10.8k parameters and requires about 5 minutes to converge on the high resolution microscopy datasets, which is orders of magnitude smaller than the relevant self-supervised methods to attain similar performance. We evaluate the proposed method for the task of segmenting nuclei from two histopathology datasets, and show comparable performance with relevant self-supervised and supervised methods.

An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis.

Bone malignant tumors are metastatic and aggressive, with poor treatment outcomes and prognosis. Rapid and accurate diagnosis is crucial for limb salvage and increasing the survival rate. There is a lack of research on deep learning to segment bone malignant tumor lesions in medical images with complex backgrounds and blurred boundaries. Therefore, we propose a new intelligent auxiliary framework for the medical image segmentation of bone malignant tumor lesions, which consists of a supervised edge-attention guidance segmentation network (SEAGNET). We design a boundary key points selection module to supervise the learning of edge attention in the model to retain fine-grained edge feature information. We precisely locate malignant tumors by instance segmentation networks while extracting feature maps of tumor lesions in medical images. The rich contextual-dependent information in the feature map is captured by mixed attention to better understand the uncertainty and ambiguity of the boundary, and edge attention learning is used to guide the segmentation network to focus on the fuzzy boundary of the tumor region. We implement extensive experiments on real-world medical data to validate our model. It validates the superiority of our method over the latest segmentation methods, achieving the best performance in terms of the Dice similarity coefficient (0.967), precision (0.968), and accuracy (0.996). The results prove the important contribution of the framework in assisting doctors to improve the accuracy of diagnosis and clinical efficiency.

Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is challenging. For this, a new fundus image segmentation framework is proposed by combining deep neural networks, and hidden Markov model. It has three main modules: the Atrous spatial pyramid pooling-based encoder, the decoder, and hidden Markov model vessel tracker. The encoder utilized modified ResNet18 deep neural networks model for low-and-high-levels features extraction. These features are concatenated in module-II by the decoder to perform convolution operations to obtain the initial segmentation. Previous modules detected the main vessel structure and overlooked some small capillaries. For improved segmentation, hidden Markov model vessel tracker is integrated with module-I and-II to detect overlooked small capillaries of the vessels network. In last module, final segmentation is obtained by combining multi-oriented sub-images using logical OR operation. This novel framework is validated experimentally using two standard DRIVE and STARE datasets. The developed model offers high average values of accuracy, area under the curve, and sensitivity of 99.8, 99.0, and 98.2%, respectively. Analysis of the results revealed that the developed approach offered enhanced performance in terms of sensitivity 18%, accuracy 3%, and specificity 1% over the state-of-the-art approaches. Owing to better learning and generalization capability, the developed approach tracked blood vessels network efficiently and automatically compared to other approaches. The proposed approach can be helpful for human eye assessment, disease diagnosis, and rehabilitation process.

Automated SAR Image Segmentation and Classification Using Modified Deep Learning

Synthetic Aperture Radar (SAR) represents a type of active remote sensing technology that uses microwave electromagnetic radiation to produce and send data to the surface of a target location. SAR imaging is frequently used in national security applications since it is unaffected by weather, geographical location, or time. In this system, many approaches are examined, to improve automation for segmentation and classification. The utilization of Deep Neural Networks (DNNs) to classify SAR images has gotten a lot of attention, and it usually requires several layers of deep models for feature learning. With insufficient training data, however, the DNN will get affected by the overfitting issue. The major purpose of this work is to make a development on introducing a new framework for SAR image segmentation and categorization using deep learning. Owing to the coherent nature of the backscattering signal, SARs create speckle noise in their images. If the image has noisy material, classification becomes more challenging. Hence, the pre-processing of the images is employed by linear spatial filtering to remove the noise. Further, the Optimized U-Net is used for the segmentation. For the segmented images, the Binary Robust Independent Elementary Features (BRIEF) concept is adopted as the feature descriptor. These features are inputted to the Convolutional Neural Network (CNN) with Tuned Weight DNN (C-TWDNN) for the classification. In both segmentation and classification, the parameter tuning is employed by the combination of Galactic Swarm Optimization (GSO) and Deer Hunting Optimization Algorithm (DHOA) called the Self-adaptive-Galactic Deer Hunting Optimization (SA-GDHO). Experiments are conducted on a variety of public datasets, demonstrating that our method is capable of outperforming various expert systems and deep structured architectures.

Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-Ray Images

As COVID-19 continues to put pressure on the global healthcare industry, using artificial intelligence to analyze chest X-rays (CXR) has become an effective way to diagnose the virus and treat patients. Despite that many studies have made significant progress in COVID-19 detection, accurately segmenting infected regions with variable locations and scales from COVID-19 CXR remains challenging. Therefore, this paper proposes a novel framework for COVID-19 CXR image segmentation. Specifically, we design a loop residual module to cyclically extract feature information in the process of encoding and decoding splicing, avoiding the loss of complex semantic information in network computing. At the same time, an absolute position information coding block is proposed to strengthen the position information of feature pixels. Moreover, a hybrid attention module is designed to establish semantic associations between channels and multi-scale spaces. Better feature representation is formed by the fusion of location and scale information to alleviate the impact of variable infection regions on segmentation performance. Extensive experiments are conducted on the public COVID-19 CXR dataset COVID-Qu-Ex, and the results show that our network is leading and robust compared to other networks in COVID-19 segmentation.

A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification

Breast ultrasound (BUS) imaging is commonly used in the early detection of breast cancer as a portable, valuable, and widely available diagnosis tool. Automated BUS image classification and segmentation can assist radiologists in making accurate and fast decisions. Recent studies illustrate that tumor, peritumoral, and background regions of BUS images provide valuable information for BUS image segmentation or classification. However, few studies have investigated the influence of these three regions on multi-task learning. In this study, we propose an RMTL-Net to simultaneously segment tumor regions and classify tumors in BUS images into benign or malignant categories. To improve both segmentation and classification performance, we design a regional attention (RA) module that employs the predicted probability maps to automatically guide the classifier to learn important category-sensitive information in the tumor, peritumoral, and background regions and seamlessly fuse them to obtain a better feature representation. We conduct detailed ablation experiments of the proposed RA module and comparative experiments with four recent state-of-the-art peer multi-task learning methods, three single-task segmentation methods, and four single-task classification methods on two public BUS datasets. Experimental results show that the proposed RMTL-Net achieves the best overall segmentation and classification accuracy in terms of five segmentation metrics and six classification metrics.

A General Global and Local Pre-Training Framework for 3D Medical Image Segmentation.

Accurate target segmentation from computed tomography (CT) scans is crucial for surgical robots to perform clinical surgeries successfully. However, the lack of medical image data and annotations has been the biggest obstacle to learning robust medical image segmentation models. Self-supervised learning can effectively address this problem by providing a strategy to pre-train a model with unlabeled data, and then fine-tune downstream tasks with limited labeled data. Existing self-supervised methods fail to simultaneously utilize the abundant global anatomical structure information and local feature differences in medical imaging. In this work, we propose a new strategy for the pre-training framework, which uses the three-dimensional anatomical structure of medical images and specific task and background cues to segment volumetric medical images with limited annotations. Specifically, we propose (1) learning intrinsic patterns of volumetric medical image structures through multiple sub-tasks, and (2) designing a multi-level background cube contrastive learning strategy to enhance the target feature representation by exploiting the differences between the specific target and background. We conduct extensive evaluations on two publicly available datasets. Under limited annotation settings, the proposed method yields significant improvements compared to other self-supervised learning techniques. The proposed method achieves within 6% of the baseline performance using only five labeled CT volumes for training. Once the paper is online, the code and dataset will be available at https://github.com/PinkGhost0812/SGL.