Biomedical Image Segmentation Tasks Research Articles

Multi-Level Object-Aware Guidance Network for Biomedical Image Segmentation

Most state-of-the-art models for biomedical image segmentation are developed based on U-shape architecture, which has two renowned, yet mutually affected, shortcomings: 1) difficulties in capturing global long-range dependencies, and 2) semantic information dilution in the decoding process. In this paper, we propose a novel network with a new object-aware module (OAM) to effectively establish global dependencies at multiple levels within the network and compensate high-level semantic information dilution when fusing the extracted multi-level features; we call the network MOG-Net. Specifically, the OAM is designed to figure out the relations between each pixel and targeting object region and recalibrate class-level semantic information according to the relations. Compared with non-local models, which construct pixel-wise global dependencies, our OAM is more efficient and target-specific, enabling us to achieve satisfactory results with less extra computational overhead. In addition, we embed a pyramid context encoder module (PCEM) in the proposed OAM to alleviate semantic information dilution; this scheme is able to bridge the spatial-semantic gap when fusing features extracted from different levels. We extensively evaluate the proposed MOG-Net on four diverse biomedical image segmentation tasks with different imaging modalities, achieving segmentation performance with 88.19%, 90.95% and 66.03% in Dice on three one-class datasets, as well as 88.83% and 87.11% in Dice for two classes on a multi-class dataset, respectively. Experimental results demonstrate the effectiveness of the proposed method, consistently outperforming state-of-the-art methods in most evaluation metrics. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Semantic segmentation of biomedical images is a critical prerequisite for subsequent diagnosis, treatment, and quantitative tasks in clinical practice. This article proposes a novel biomedical image segmentation network, namely MOG-Net, with a new object-aware module (OAM) to model global context dependencies from a category perspective and a pyramid context encoder module (PCEM) to enhance feature representation capabilities of spatial and channel dimensions. We experimentally demonstrate the effectiveness and generalization capability of proposed MOG-Net on diverse biomedical image segmentation tasks with different imaging modalities. We believe that our proposed method can serve as a practical clinical tool and has the potential to be applied to existing computer-aided medical systems and clinical measurement.

English

U-Net is adequate for various biomedical image segmentation tasks. Using this in the Age-related Macular Degeneration (AMD) images would be better for segmenting only the affected region. The variant of U-Net uses residual connections. A residual link is a type of connection that allows the network to learn the residual mapping between the input and output rather than the complete mapping. This technique can help the network learn more efficiently and avoid the vanishing gradients problem. This research aims to develop a method for segmenting the retinal pigment epithelium, the Bruch’s membrane, and the Inner Limiting Membranes (ILM) in OCT scans of individuals in good health and with intermediate AMD.

Context Prior Guided Semantic Modeling for Biomedical Image Segmentation

Most state-of-the-art deep networks proposed for biomedical image segmentation are developed based on U-Net. While remarkable success has been achieved, its inherent limitations hinder it from yielding more precise segmentation. First, its receptive field is limited due to the fixed kernel size, which prevents the network from modeling global context information. Second, when spatial information captured by shallower layer is directly transmitted to higher layers by skip connections, the process inevitably introduces noise and irrelevant information to feature maps and blurs their semantic meanings. In this article, we propose a novel segmentation network equipped with a new context prior guidance (CPG) module to overcome these limitations for biomedical image segmentation, namely context prior guidance network (CPG-Net). Specifically, we first extract a set of context priors under the supervision of a coarse segmentation and then employ these context priors to model the global context information and bridge the spatial-semantic gap between high-level features and low-level features. The CPG module contains two major components: context prior representation (CPR) and semantic complement flow (SCF). CPR is used to extract pixels belonging to the same objects and hence produce more discriminative features to distinguish different objects. We further introduce deep semantic information for each CPR by the SCF mechanism to compensate the semantic information diluted during the decoding. We extensively evaluate the proposed CPG-Net on three famous biomedical image segmentation tasks with diverse imaging modalities and semantic environments. Experimental results demonstrate the effectiveness of our network, consistently outperforming state-of-the-art segmentation networks in all the three tasks. Codes are available at https://github.com/zzw-szu/CPGNet .

Modified Double U-Net Architecture for Medical Image Segmentation

Semantic segmentation is the process of labeling each pixel of an input image with its appropriate class. In biomedical image processing, semantic segmentation is a critical preprocessing step. After the evolution of deep learning, specifically, the U-Net, which is based on the encoder–decoder model, researchers have extensively used several modified versions of U-Net for semantic segmentation. This manuscript presents a novel architecture called modified Double U-Net for various biomedical image segmentation tasks. The modified Double U-Net takes full advantage of Double U-Net and ensemble learning. It is the combination of two U-Net architectures stacked on top of each other. The first U-Net uses an ensemble of pretrained Xception, DenseNet, and VGG-19. We have deliberately used a pretrained model so that the features learned from the ImageNet classification task can be reused for our semantic segmentation task. Another U-Net is stacked at the bottom to capture more information that can be used in our semantic segmentation task. The performance of our proposed model has been evaluated on five different medical datasets, namely, the Data Science Bowl Challenge-2018, the CVC-ClinicDB dataset, which has complex images, such as smaller and flat polyps, the ISIC-2018 challenge dataset, the Kvasir-Instrument dataset, and the INBreast dataset achieving state-of-the-art performance. Conducting fivefold cross-validation on the five datasets mentioned above intersection over union of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$89.13~\pm ~2.88$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$85.22~\pm ~1.26$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$83.13~\pm ~3.56$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$85.22~\pm ~1.26$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$95.22~\pm ~2.56$ </tex-math></inline-formula> were obtained.

Attention Mechanism Trained with Small Datasets for Biomedical Image Segmentation

The understanding of long-range pixel–pixel dependencies plays a vital role in image segmentation. The use of a CNN plus an attention mechanism still has room for improvement, since existing transformer-based architectures require many thousands of annotated training samples to model long-range spatial dependencies. This paper presents a smooth attention branch (SAB), a novel architecture that simplifies the understanding of long-range pixel–pixel dependencies for biomedical image segmentation in small datasets. The SAB is essentially a modified attention operation that implements a subnetwork via reshaped feature maps instead of directly calculating a softmax value over the attention score for each input. The SAB fuses multilayer attentive feature maps to learn visual attention in multilevel features. We also introduce position blurring and inner cropping specifically for small-scale datasets to prevent overfitting. Furthermore, we redesign the skip pathway for the reduction of the semantic gap between every captured feature of the contracting and expansive path. We evaluate the architecture of U-Net with the SAB (SAB-Net) by comparing it with the original U-Net and widely used transformer-based models across multiple biomedical image segmentation tasks related to the Brain MRI, Heart MRI, Liver CT, Spleen CT, and Colonoscopy datasets. Our training set was made of random 100 images of the original training set, since our goal was to adopt attention mechanisms for biomedical image segmentation tasks with small-scale labeled data. An ablation study conducted on the brain MRI test set demonstrated that every proposed method achieved an improvement in biomedical image segmentation. Integrating the proposed methods helped the resulting models consistently achieve outstanding performance on the above five biomedical segmentation tasks. In particular, the proposed method with U-Net improved its segmentation performance over that of the original U-Net by 13.76% on the Brain MRI dataset. We proposed several novel methods to address the need for modeling long-range pixel–pixel dependencies in small-scale biomedical image segmentation. The experimental results illustrated that each method could improve the medical image segmentation accuracy to various degrees. Moreover, SAB-Net, which integrated all proposed methods, consistently achieved outstanding performance on the five biomedical segmentation tasks.

AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images.

Semantic segmentation of biomedical images found its niche in screening and diagnostic applications. Recent methods based on deep learning convolutional neural networks have been very effective, since they are readily adaptive to biomedical applications and outperform other competitive segmentation methods. Inspired by the U-Net, we designed a deep learning network with an innovative architecture, hereafter referred to as AID-U-Net. Our network consists of direct contracting and expansive paths, as well as a distinguishing feature of containing sub-contracting and sub-expansive paths. The implementation results on seven totally different databases of medical images demonstrated that our proposed network outperforms the state-of-the-art solutions with no specific pre-trained backbones for both 2D and 3D biomedical image segmentation tasks. Furthermore, we showed that AID-U-Net dramatically reduces time inference and computational complexity in terms of the number of learnable parameters. The results further show that the proposed AID-U-Net can segment different medical objects, achieving an improved 2D F1-score and 3D mean BF-score of 3.82% and 2.99%, respectively.

OAU-net: Outlined Attention U-net for biomedical image segmentation

In this paper, we propose an Outlined Attention U-network (OAU-net) with bypass branching strategy to solve biomedical image segmentation tasks, which is capable of sensing shallow and deep features. Unlike previous studies, we use residual convolution and res2convolution as encoders. In particular, the outline filter and attention module are embedded in the skip connection part, respectively. Shallow features will enhance the edge information after being processed by the outline filter. Meanwhile, in the depths of the network, to better realize feature fusion, our attention module will simultaneously emphasize the independence between feature map channels (channel attention module) and each position information (spatial attention module), that is, the hybrid domain attention module. Finally, we conducted ablation experiments and comparative experiments according to three public data sets (pulmonary CT lesions, Kaggle 2018 data science bowl, skin lesions), and analyzed them with classical evaluation indexes. Experimental results show that our proposed method improves segmentation accuracy effectively. Our code is public at https://github.com/YF-W/OAU-net.

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey.

Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.

Multi-Scale Dilated Fusion Network (MSDFN) for Automatic Instrument Segmentation

With the recent advancements in the field of semantic segmentation, an encoderdecoder approach like U-Net are most widely used to solve biomedical image segmentation tasks. To improve upon the existing U-Net, we proposed a novel architecture called Multi-Scale Dilated Fusion Network (MSDFNet). In this work, we have used the pre-trained ResNet50 as the encoder, which had already learned features that can be used by the decoder to generate the binary mask. In addition, we used skip-connections to directly facilitate the transfer of features from the encoder to the decoder. Some of these features are lost due to the depth of the network. The decoder consists of a Multi-Scale Dilated Fusion block, as the main components of the decoder, where we fused the multiscale features and then applied some dilated convolution upon them. We have trained both the U-Net and the proposed architecture on the Ksavir-Instrument dataset, where the proposed architecture has a 3.701 % gain in the F1 score and 4.376 % in the Jaccard. These results show the improvement over the existing U-Net model.

Dynamic Linear Transformer for 3D Biomedical Image Segmentation.

Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism. However, most methods are still designed for 2D medical images while ignoring the essential 3D volume information. The main challenge for 3D Transformer-based segmentation methods is the quadratic complexity introduced by the self-attention mechanism [17]. In this paper, we are addressing these two research gaps, lack of 3D methods and computational complexity in Transformers, by proposing a novel Transformer architecture that has an encoder-decoder style architecture with linear complexity. Furthermore, we newly introduce a dynamic token concept to further reduce the token numbers for self-attention calculation. Taking advantage of the global information modeling, we provide uncertainty maps from different hierarchy stages. We evaluate this method on multiple challenging CT pancreas segmentation datasets. Our results show that our novel 3D Transformer-based segmentor could provide promising highly feasible segmentation performance and accurate uncertainty quantification using single annotation. Code is available https://github.com/freshman97/LinTransUNet.

Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

BackgroundColonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems to support clinicians and reduce the number of polyps missed. MethodIn this work we introduce the Focus U-Net, a novel dual attention-gated deep neural network, which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features. The Focus U-Net incorporates several further architectural modifications, including the addition of short-range skip connections and deep supervision. Furthermore, we introduce the Hybrid Focal loss, a new compound loss function based on the Focal loss and Focal Tversky loss, designed to handle class-imbalanced image segmentation. For our experiments, we selected five public datasets containing images of polyps obtained during optical colonoscopy: CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, ETIS-Larib PolypDB and EndoScene test set. We first perform a series of ablation studies and then evaluate the Focus U-Net on the CVC-ClinicDB and Kvasir-SEG datasets separately, and on a combined dataset of all five public datasets. To evaluate model performance, we use the Dice similarity coefficient (DSC) and Intersection over Union (IoU) metrics. ResultsOur model achieves state-of-the-art results for both CVC-ClinicDB and Kvasir-SEG, with a mean DSC of 0.941 and 0.910, respectively. When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0.878 and mean IoU of 0.809, a 14% and 15% improvement over the previous state-of-the-art results of 0.768 and 0.702, respectively. ConclusionsThis study shows the potential for deep learning to provide fast and accurate polyp segmentation results for use during colonoscopy. The Focus U-Net may be adapted for future use in newer non-invasive colorectal cancer screening and more broadly to other biomedical image segmentation tasks similarly involving class imbalance and requiring efficiency.

Retinal Based Pathology Analysis Using Deep Learning Approaches

: Deep learning is an excellent approach to biomedical image segmentation tasks that help to identify and quantify patterns. Segmentation of biomedical images typically involves partitioning an image into multiple regions representing anatomical objects of interest. Biomedical image processing is a very broad field. Here, input is given as an image to extract features from an image to detect diseases. Unsupervised learning technique is used because it enables the model to uncover trends and facts that were previously undetected on its own. Human eye retina can provide valuable information about human health. The condition of the retinal vessels has been found to be a good indicator of overall health. For the prediction of retinal disease, it is classified as a retinal background, retinal vessels and optic disc. The disease can be predicted by this classification using the Singular Spectrum Analysis, Deep Neural Network, KNN Classifier and Eclipse Fitting algorithm. Singular Spectrum analysis was carried out to predict cross-section profiles for the detection of micro aneurysms. Diabetic diseases are classified under the KNN classification. Deep Neural Network algorithm is used for predicting cardiovascular disease and other disease such as glaucoma, stroke. Using the Eclipse Fitting algorithm, the optical disc and cup pixels are segmented to check whether or not a person's health is normal.

PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

BackgroundWith the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing.ResultsA lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters.ConclusionsOur experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies.

Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources’ requirements. Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.

A Stacked Generalization U-shape network based on zoom strategy and its application in biomedical image segmentation

Background and objective: The deep neural network model can learn complex non-linear relationships in the data and has superior flexibility and adaptability. A downside of this flexibility is that they are sensitive to initial conditions, both in terms of the initial random weights and in terms of the statistical noise in the training dataset. And the disadvantage caused by adaptability is that deep convolutional networks usually have poor robustness or generalization when the models are trained using the extremely limited amount of labeled data, especially in the biomedical imaging informatics field.Methods: In this paper, we propose to develop and test a stacked generalization U-shape network (SG-UNet) based on the zoom strategy applying to biomedical image segmentation. SG-UNet is essentially a stacked generalization architecture consisting of multiple sub-modules, which takes multi-resolution images as input and uses hybrid features to segment regions of interest and detect diseases under the multi-supervision. The proposed new SG-UNet applies the zoom of multi-supervision to do optimization search in global feature space without pre-training. Besides, the zoom loss function can gradually enhance the focus training on a sparse set of hard samples.Results: We evaluated the proposed algorithm in comparison with several popular U-shape ensemble network architectures across multi-modal biomedical image segmentation tasks to segment malignant rectal cancers, polyps and glands from the three imaging modalities of computed tomography (CT), digital colonoscopy and histopathology images. Applying the proposed algorithm improves 3.116%, 2.676%, 2.356% on Dice coefficients, and 3.044%, 2.420%, 1.928% on F2-score for the three imaging modality datasets, respectively. The comparison results using different amounts of rectal cancer CT data show that the proposed algorithm has a slower tendency of diminishing marginal efficiency. And glands segmentation study results also support the feasibility of yielding comparable performance with other state-of-the-art methods.Conclusions: The proposed algorithm can be trained more efficiently by using the small image datasets without using additional techniques such as fine-tuning, and achieves higher accuracy with less computational complexity than other stacked ensemble networks for biomedical image segmentation.

Fully convolutional attention network for biomedical image segmentation

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image segmentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances. Specifically, we add two types of attention modules, the spatial attention module and the channel attention module, to the Res2Net network, which has a dilated strategy. The features of each location are aggregated through the spatial attention module, so that similar features promote each other in space size. At the same time, the channel attention module treats each channel of the feature map as a feature detector and emphasizes the channel dependency between any two channel maps. Finally, we weight the sum of the output features of the two types of attention modules to retain the feature information of the long-range and short-range distances, to further improve the representation of the features and make the biomedical image segmentation more accurate. In particular, we verify that the proposed attention module can seamlessly connect to any end-to-end network with minimal overhead. We perform comprehensive experiments on three public biomedical image segmentation datasets, i.e., the Chest X-ray collection, the Kaggle 2018 data science bowl and the Herlev dataset. The experimental results show that FCANet can improve the segmentation effect of biomedical images. The source code models are available at https://github.com/luhongchun/FCANet.

Non-Local U-Nets for Biomedical Image Segmentation

Deep learning has shown its great promise in various biomedical image segmentation tasks. Existing models are typically based on U-Net and rely on an encoder-decoder architecture with stacked local operators to aggregate long-range information gradually. However, only using the local operators limits the efficiency and effectiveness. In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation. These blocks can be inserted into U-Net as size-preserving processes, as well as down-sampling and up-sampling layers. We perform thorough experiments on the 3D multimodality isointense infant brain MR image segmentation task to evaluate the non-local U-Nets. Results show that our proposed models achieve top performances with fewer parameters and faster computation.

ResWnet for Retinal Small Vessel Segmentation

U-Net shows excellent performance in biomedical image segmentation tasks, Variants of U-Net have been proposed, such as U-Net with residual blocks, densely connected convolution, or deformed convolution. However, contracting path of these variants is containing four consecutive downsampling layers and an encoding-decoding structure with skip connections. The feature map transformation paths are limited. Therefore, ResWnet is proposed in this work, the number of downsampling layers of contracting path is modified to two, and the network is changed from the encoding-decoding structure to the encoding-decoding-encoding-decoding structure so that the network can retain more detailed features and extract deeper semantic information. The skip connections are used between the contracting path and the expansion path at the same scale to increase the information transmission path. The convergence speed of the network is accelerated by the residual block replacing convolution layer. The image is scaled to different scales and is inputted into the network from the left to enhance the sensitivity of the network for blood vessels at different scales. The feature maps extracted from different layers of the network are scaled to the same size as the inputted image for fusion and segmentation. The performance of ResWnet is evaluated on DRIVE and STARE databases. The AUC is 0.9799 and 0.9863, and the accuracy is 0.9554 and 0.9723, respectively. According to the results, ResWnet outperforms many other proposed methods in small vessel segmentation. For future research, it can be learned that pre-processing and post-processing are vital to the segmentation accuracy. Furthermore, ResWnet can also be applied to other semantic segmentation tasks such as lung nodule segmentation or cancer cell segmentation. For future research, the pre-processing and post-processing will be tried to further enhance the segmentation accuracy performance of ResWnet.

DRU‐net: a novel U‐net for biomedical image segmentation

With the wide applications of biomedical images in the medical field, the segmentation of biomedical images plays an important role in clinical diagnosis, pathological analysis, and medical intervention. Full convolutional neural networks, especially U-net, have improved the performance of segmentation greatly in recent years. However, due to their regular geometric structure, the standard convolutions that they use are inherently limited in dealing with geometric transformations while biomedical objects have huge variations in shape and size. In this study, the authors propose the DRU-net, which is a novel U-net with deformable encoder and reshaping upsampling convolution decoder, for biomedical image segmentation. First, deformable convolutional networks are applied and improved to enhance the learning ability of the encoder for geometric transformations. Second, a novel upsampling method named reshape upsampling convolution is proposed for better-restoring resolution and fusion features. Furthermore, focal loss is used to address class imbalance and model overwhelmed problems in biomedical image segmentation tasks. Theoretic analysis and experimental results have shown that the proposed algorithm not only reduces the number of parameters of U-Net, but also achieves produces competitive results compared with the state-of-the-art algorithms in terms of various quantitative measures on Drosophila electron microscopy dataset and Warwick-QU dataset.

Biomedical Image Segmentation via Representative Annotation

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.