Automatic Skin Lesion Segmentation Research Articles

Automated skin lesion segmentation using attention-based deep convolutional neural network

Edge detection for dermoscopic images has always been a crucial task for automatic lesion delineation processes. A skin lesion is an area of the skin that takes the form an abnormal growth or appearance when compared to the skin surrounding it. The abnormal appearance is the colored area of the skin that is advised for urgent referral and treatment. The manual way of diagnosing the disease is time-consuming and not quantifiable. However, computer-aided diagnosis (CADx)-based treatment can provide aid to manual delineation by the experts in diagnosing the disease with more proficiency. To advance the digital process of segmentation, a deep learning-based end-to-end framework is proposed for automatic dermoscopic image segmentation. The framework has the modified form of U-Net, which effectively uses Group Normalization (GN) in the encoder and the decoder layers. Attention Gates (AG) focusing on minute details in the skip connection later incorporates with Tversky Loss (TL) as the output loss function are added. Instead of Batch Normalization (BN), GN is used to extract the feature maps generated by the encoding path efficiently. To distinguish high dimensional information from low-level irrelevant background regions in the input image, AGs are used. Tversky Index (TI)-based TL is applied to accomplish better alliance between recall and precision. To further strengthen feature propagation and encourage feature reuse, atrous convolutions are applied in the connecting bridge between the encoder path and the decoder path of the network. The proposed model is evaluated on the ISIC 2018 image dataset, outshone the state-of-the-art segmentation methods.

Correction to: The novel multi-swarm coyote optimization algorithm for automatic skin lesion segmentation

Correction to: The novel multi-swarm coyote optimization algorithm for automatic skin lesion segmentation

Automatic Skin Lesion Segmentation—A Novel Approach of Lesion Filling through Pixel Path

Lesion segmentation is a vital step in a melanoma recognition system. Many algorithms were developed for the efficient skin lesion segmentation. Most of them fails to realize a perfect segmentation. This paper proposes a novel, fully automatic system, for the lesion segmentation in dermatograms. The proposed approach executes in two steps. Selection of root seed is the first step. All the lesion pixels in the dermatogram are identified during the second step. Traversal through a predefined lesion pixel path ensures the reachability of all lesion pixels irrespective of the possible lesion discontinuity. The proposed algorithm is tested with two publically available dataset, PH2 and images of ISBI2016 challenge. Out of the six evaluation parameters, the proposed method shows the best values for specificity, accuracy, Hammuode distance and XOR. This confirms the merit of the proposal with respect to existing popular methods.

Deep learning based an automated skin lesion segmentation and intelligent classification model

Internet of Medical Things (IoMT) includes interconnected sensors, wearable devices, medical devices, and clinical systems. At the same time, skin cancer is a commonly available type of cancer that exists all over the globe. This study projects a new segmentation based classification model for skin lesion diagnosis by combining a GrabCut algorithm and Adaptive Neuro-Fuzzy classifier (ANFC) model. The proposed method involves four main steps: preprocessing, segmentation, feature extraction, and classification. Initially, the preprocessing step is carried out using a Top hat filter and inpainting technique. Then, the Grabcut algorithm is used to segment the preprocessed images. Next, the feature extraction process takes place by the use of a deep learning based Inception model. Finally, an adaptive neuro-fuzzy classifier (ANFC) system gets executed to classify the dermoscopic images into different classes. The proposed model is simulated using a benchmark International Skin Imaging Collaboration (ISIC) dataset and the results are examined interms of accuracy, sensitivity and specificity. The proposed model exhibits better identification and classification of skin cancer. For examining the effective outcome of the projected technique, an extensive comparison of the presented method with earlier models takes place. The experimental values indicated that the proposed method has offered a maximum sensitivity of 93.40%, specificity of 98.70% and accuracy of 97.91%.

Fast fully automatic skin lesions segmentation probabilistic with Parzen window

Cutaneous melanoma accounts for over 90% of all melanoma, causing up to 55,500 annual deaths. However, it is a potentially curable type of cancer. Since melanoma is potentially curable, the disease’s mortality rate is directly linked to late detection. This work proposes an approach that presents the balance between time and efficiency. This paper proposes the method of fast and automatic segmentation of skin lesions using probabilistic characteristics with the Parzen window (SPPW). The results obtained by the method were based on PH2 and ISIC datasets. The SPPW approach reached the following averages between the two datasets Specificity of 98.55%, Accuracy of 95.48%, Dice of 91.12%, Sensitivity of 88.45%, Mattheus of 87.86%, and Jaccard Index of 84.90%. The highlights of the proposed method are its short average segmentation time per image, and its metrics values, which are often higher than the ones obtained by other methods. Therefore, the SPPW method of segmentation is a quick, viable, and easily accessible option to aid in the diagnosis of diseased skin.

The novel multi-swarm coyote optimization algorithm for automatic skin lesion segmentation

Coyote optimization algorithm (COA) is one of population-based swarm intelligence algorithms inspired by the swarming behavior of coyotes. However, COA showed its effectiveness in solving the global optimization problem, it suffers from premature convergence and stagnation in local optima, espicially in a complex space. In this paper, the multi-swarm topology is employed, where the population is divided into several sub-swarms. The performance of multi-swarm coyote optimization algorithm (MCOA) is evaluated on a set of benchmark functions provided in the IEEE CEC 2005 and IEEE CEC 2017 special sessions. Also, it is evaluated for solving multi-level thresholding problem, where 44 skin dermoscopic images obatined from PH2 benchmark dataset are used. The experimental results showed that employing mutli-swarm topology can significantly improve the population diversity and thus the exploration ability. Also, the results reveal that proposed MCOA has the advantages of remarkable stability and high accuracy compared with its classical version and other state-of-art meta-heuristic optimization algorithms. Additionally, a new skin lesion segmentation model based on MCOA is proposed as well. The results illustrate the effectiveness and efficiency of the proposed model and it can be further used for skin disease diagnosis and treatment planning.

Skin lesion segmentation using fully convolutional networks: A comparative experimental study

Because the most dangerous type of skin cancer, melanoma, is very difficult for dermatologists to detect because of the low contrast between the lesion and the adjacent skin, the automatic application of skin lesion segmentation is regarded as very challenging. This paper proposes the implementation of a medical image segmentation that will accelerate a melanoma diagnosis by dermatologists. In the implementation, Fully Convolutional Network (FCN) architectures generated by modifying Convolutional Neural Network (CNN) architectures are used. The proposed algorithm for an automatic semantic segmentation of skin lesions utilizes four different FCN architectures, FCN-AlexNet, FCN-8s, FCN-16s, and FCN-32s. The experimental studies in this paper are constructed on the ISIC 2017 dataset, and the evaluations of these architectures on the dataset are carried out for the first time with this study. In the experimental studies, once the images in the dataset are preprocessed, the FCNs are first trained separately. Secondly, the accuracies and Dice coefficients on the validation dataset are calculated by using these trained FCN architectures. Thirdly, the obtained results are compared. Finally, the inferences of lesion segmentation are visualized in order to exhibit how exactly the FCN architectures can segment the lesions. The experimental results show that the FCNs in the proposed algorithm are suitable for skin lesion segmentation. In addition, it is thought that the experimental results will contribute to the scientific literature and assist the researchers who are working on medical image segmentation.

Automatic skin lesion segmentation based on FC-DPN

Automatic skin lesion segmentation in dermoscopy images is challenging due to the diversity of skin lesion characteristics, low contrast between normal skin and lesions, and the existence of many artefacts in the images. To meet these challenges, we propose a novel segmentation topology called FC-DPN, which is built upon a fully convolutional network (FCN) and dual path network (DPN). The DPN inherits the advantages of residual and densely connected paths, enabling effective feature re-usage and re-exploitation. We replace dense blocks in fully convolutional DenseNets (FC-DenseNets) with two kinds of sub-DPN blocks, namely, sub-DPN projection blocks and sub-DPN processing blocks. This framework enables FC-DPN to acquire more representative and discriminative features for more accurate segmentation. Many images in the original ISBI 2017 Skin Lesion Challenge test dataset are given the incorrect or inaccurate ground truths, and these ground truths have been revised. The revised test dataset is called the modified ISBI 2017 Skin Lesion Challenge test dataset. The proposed method achieves an average Dice coefficient of 88.13% and a Jaccard index of 80.02% on the modified ISBI 2017 Skin Lesion Challenge test dataset and 90.26% and 83.51%, respectively, on the PH2 dataset. Extensive experimental results on the two datasets demonstrate that the proposed method exhibits better performance than FC-DenseNets and other well-established segmentation algorithms.

DSNet: Automatic dermoscopic skin lesion segmentation

Background and Objective:Automatic segmentation of skin lesions is considered a crucial step in Computer-aided Diagnosis (CAD) systems for melanoma detection. Despite its significance, skin lesion segmentation remains an unsolved challenge due to their variability in color, texture, and shapes and indistinguishable boundaries. Methods:Through this study, we present a new and automatic semantic segmentation network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet). In order to reduce the number of parameters to make the network lightweight, we used a depth-wise separable convolution in lieu of standard convolution to project the learned discriminating features onto the pixel space at different stages of the encoder. Additionally, we implemented both a U-Net and a Fully Convolutional Network (FCN8s) to compare against the proposed DSNet. Results:We evaluate our proposed model on two publicly available datasets, namely ISIC-201711https://challenge.kitware.com/#challenge/583f126bcad3a51cc66c8d9a. and PH222https://www.fc.up.pt/addi/ph2%20database.html.. The obtained mean Intersection over Union (mIoU) is 77.5% and 87.0% respectively for ISIC-2017 and PH2 datasets which outperformed the ISIC-2017 challenge winner by 1.0% with respect to mIoU. Our proposed network also outperformed U-Net and FCN8s respectively by 3.6% and 6.8% with respect to mIoU on the ISIC-2017 dataset. Conclusion:Our network for skin lesion segmentation outperforms the other methods discussed in the article and is able to provide better-segmented masks on two different test datasets which can lead to better performance in melanoma detection. Our trained model along with the source code and predicted masks are made publicly available33https://github.com/kamruleee51/Skin-Lesion-Segmentation-Using-Proposed-DSNet..

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification.

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

Skin Lesion Segmentation Based on Multi-Scale Attention Convolutional Neural Network

The incidence of skin cancer around the world is increasing year by year. However, early diagnosis and treatment can greatly improve the survival rate of patients. Skin lesion boundary segmentation is essential to accurately locate lesion areas in dermatoscopic images. It is true that accurate segmentation of skin lesions is still challenging dues to problems such as blurred borders, which requires an accurate and automatic skin lesion segmentation method. In this paper, we propose an end-to-end framework which can perform skin lesion segmentation automatically and efficiently, called the CSARM-CNN (Channel & Spatial Attention Residual Module) model. Each CSARM block of the model combines channel attention and spatial attention to form a new attention module to enhance segmentation results. The multi-scale input images are obtained by the spatial pyramid pooling. Finally, a weighted cross-entropy loss function is used at each side of the output layer to sum the total loss of the model. We evaluated in two published standard datasets, ISIC 2017 and PH2, and achieved competitive results in terms of specificity and accuracy, with 99.03% and 99.45% specificity, 94.96% and 95.23% accuracy, respectively.

OCE-NGC: A neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation

Automated skin lesion segmentation is one of the most crucial stages in dermoscopic images based diagnosis. To guarantee efficient unsupervised clustering-based segmentation, a histogram-based clustering estimation (HBCE) algorithm can be used to obtain the initial number of clusters with their corresponding centroids. Accordingly, the present work introduced a novel skin lesion segmentation algorithm, called optimized clustering estimation for neutrosophic graph cut algorithm (OCE-NGC). Firstly, the genetic algorithm (GA) is used to optimize the HBCE procedure by finding its optimal threshold values which are functions of a factor, called β to be optimized. This optimization process guarantees the optimal determination of the initial number of clusters and their corresponding centroids for further use in the proposed clustering process. Thus, the skin lesion dermoscopic images are then mapped into the neutrosophic set (NS) domain which is computed by the neutrosophic c-means (NCM). The NCM groups the pixels in the dermoscopic images using the pre-determined optimal number of clusters obtained by the optimized HBCE. Finally, a cost function of the graph cut (GC) algorithm is defined in the NS domain for the segmentation process. The experimental results established the superiority of the proposed OCE-NGC approach in comparison with the traditional HBCE with NCM only, the traditional HBCE with the NGC, and the typical GC. In a public dataset, the proposed approach achieved 97.12% and 86.28% average accuracy and average Jaccard (JAC) values, respectively.

Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm.

Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.

Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM)

Objective:The main objective of this study is to improve the classification performance of melanoma using deep learning based automatic skin lesion segmentation. It can be assist medical experts on early diagnosis of melanoma on dermoscopy images.Methods:First A Convolutional Neural Network (CNN) based U-net algorithm is used for segmentation process. Then extract color, texture and shape features from the segmented image using Local Binary Pattern ( LBP), Edge Histogram (EH), Histogram of Oriented Gradients (HOG) and Gabor method. Finally all the features extracted from these methods were fed into the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) classifiers to diagnose the skin image which is either melanoma or benign lesions.Results:Experimental results show the effectiveness of the proposed method. The Dice co-efficiency value of 77.5% is achieved for image segmentation and SVM classifier produced 85.19% of accuracy.Conclusion:In deep learning environment, U-Net segmentation algorithm is found to be the best method for segmentation and it helps to improve the classification performance.

Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons.

Segmentation of skin lesions is an important step in computer-aided diagnosis of melanoma; it is also a very challenging task due to fuzzy lesion boundaries and heterogeneous lesion textures. We present a fully automatic method for skin lesion segmentation based on deep fully convolutional networks (FCNs). We investigate a shallow encoding network to model clinically valuable prior knowledge, in which spatial filters simulating simple cell receptive fields function in the primary visual cortex (V1) is considered. An effective fusing strategy using skip connections and convolution operators is then leveraged to couple prior knowledge encoded via shallow network with hierarchical data-driven features learned from the FCNs for detailed segmentation of the skin lesions. To our best knowledge, this is the first time the domain-specific hand craft features have been built into a deep network trained in an end-to-end manner for skin lesion segmentation. The method has been evaluated on both ISBI 2016 and ISBI 2017 skin lesion challenge datasets. We provide comparative evidence to demonstrate that our newly designed network can gain accuracy for lesion segmentation by coupling the prior knowledge encoded by the shallow network with the deep FCNs. Our method is robust without the need for data augmentation or comprehensive parameter tuning, and the experimental results show great promise of the method with effective model generalization compared to other state-of-the-art-methods.

Attention-Based DenseUnet Network With Adversarial Training for Skin Lesion Segmentation

Automatic segmentation of skin lesions in dermoscopy images is a challenging task due to the large size and shape variations of the lesions, the existence of various artifacts, the low contrast between the lesion and the surrounding skin. In this paper, we propose a novel Attention Based DenseUnet network (referred as Att-DenseUnet) with adversarial training for skin lesion segmentation. Att-DenseUnet is a Generative Adversarial Network which contains two major components: Segmentor and Discriminator. In the Segmentor module, we propose an architecture which is similar to DenseNet in the down-sampling path to ensure maximum multi-scale skin lesions information transfer between layers in the network at dense scale range, meanwhile, we design an attention module to automatically focus on the skin lesion features and suppress the irrelevant artifacts features in the output feature maps of the DenseBlocks. In the Discriminator module, we employ adversarial feature matching loss to train the Segmentor stably, force the Segmentor to extract multi-scale discriminative features, and guide the attention module focusing on the multi-scale skin lesions. A novel loss function of the Segmentor is proposed which combines the jaccard distance loss with the adversarial feature matching loss introduced by the Discriminator. We trained the proposed Att-DenseUnet on ISBI2017 dataset. The test results show that our approach gains the state-of-the-art performance, especially for JAC (0.8045) and SEN (0.8734) scores which are significantly improved by 2.2% and 1.9%, respectively, also our network is robust to different datasets, and gains the lowest time cost which make our network suitable for clinical application.

Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation

Background and objectiveFully convolutional neural networks have been shown to perform well for automated skin lesion segmentation on digital dermatoscopic images. Our concept is that transferring encoder weights from a network trained on a classification task on images of the same domain may contain useful information for segmentation. MethodsWe trained a fully convolutional network where ResNet34 layers are reused as encoding layers of a U-Net style architecture. We entered the encoding layers i) with He uniform (“random”) initialization, ii) pretrained ImageNet weights, or iii) after fine-tuning ResNet34 for skin lesion classification. After transferring the layers to the fully convolutional network architecture we trained for a binary segmentation task using official ISIC 2017 challenge data. ResultsPretraining of ResNet34-layers with either ImageNet or fine-tuning for skin lesion classification achieved a higher Jaccard than random initialization (0.763 and 0.768 vs 0.740) on the ISIC 2017 test-set. This improved performance warrants further exploration on how to implement cross-task learning for skin lesion segmentation. In additional experiments we found that post-processing with fully connected conditional random fields consistently decreased Jaccard on ISIC 2017 test-set images despite reasonable visual results. Further exploration of the test-set revealed that conditional random field - post-processing decreased segmentation performance only if ground truth annotations consisted of simple shapes but increased it if shapes were complex. ConclusionsOur findings suggest that domain specific pretraining of encoders can be helpful when there are only few ground truth masks available for segmentation training, but may not be of additional benefit to ImageNet pretraining given enough segmentation training data. Complexity of ground truth annotations have a large impact on segmentation metrics and should be taken into account in skin lesion segmentation research.

Step-wise integration of deep class-specific learning for dermoscopic image segmentation

The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very successful for dermoscopic image segmentation. However, they tend to overfit to the visual characteristics that are present in the dominant non-melanoma studies and therefore, perform poorly on the complex visual characteristics exhibited by melanoma studies, which usually consists of fuzzy boundaries and heterogeneous textures. In this paper, we propose a new method for automated skin lesion segmentation that overcomes these limitations via a novel deep class-specific learning approach which learns the important visual characteristics of the skin lesions of each individual class (melanoma vs. non-melanoma) on an individual basis. We also introduce a new probability-based, step-wise integration to combine complementary segmentation results derived from individual class-specific learning models. We achieved an average Dice coefficient of 85.66% on the ISBI 2017 Skin Lesion Challenge (SLC), 91.77% on the ISBI 2016 SLC and 92.10% on the PH2 datasets with corresponding Jaccard indices of 77.73%, 85.92% and 85.90%, respectively, for the same datasets. Our experiments on three well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation.

Automated skin lesion segmentation of dermoscopic images using GrabCut and k‐means algorithms

Skin cancer is the most common type of cancer in the world and the incidents of skin cancer have been rising over the past decade. Even with a dermoscopic imaging system, which magnifies the lesion region, detecting and classifying skin lesions by visual examination is laborious due to the complex structures of the lesions. This necessitates the need for an automated skin lesion diagnosis system to enhance the diagnostic capability of dermatologists. In this study, the authors propose an automatic skin lesion segmentation method which can be used as a preliminary step for lesion classification. The proposed method comprises two major steps, namely preprocessing and segmentation. In the preprocessing step, noise such as illumination, hair and rulers are removed using filtering techniques and in the segmentation phase, skin lesions are segmented using the GrabCut segmentation algorithm. The k‐means clustering algorithm is then used along with the colour features learnt from the training images to improve the boundaries of the segments. To evaluate the authors’ proposed method, they have used ISIC 2017 challenge dataset and dataset. They have obtained Dice coefficient values of 0.8236 and 0.9139 for ISIC 2017 test dataset and dataset, respectively.

Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks.

Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is further exacerbated when dealing with a large amount of image data. In this paper, we extended our previous work by developing a deeper network architecture with smaller kernels to enhance its discriminant capacity. In addition, we explicitly included color information from multiple color spaces to facilitate network training and thus to further improve the segmentation performance. We participated and extensively evaluated our method on the ISBI 2017 skin lesion segmentation challenge. By training with the 2000 challenge training images, our method achieved an average Jaccard Index (JA) of 0:765 on the 600 challenge testing images, which ranked itself in the first place among 21 final submissions in the challenge.