Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation

Mirikharaji, Zahra; Hamarneh, Ghassan

doi:10.1007/978-3-030-00937-3_84

Cited by 98 publications

(55 citation statements)

References 25 publications

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“…We compared the proposed MB-DCNN model to several recently published skin lesion segmentation methods in Table I. On the ISIC-2017 dataset, the competing methods include a convolutional-deconvolutional neural network (CDNN) [8], a new dense deconvolutional network (DDN) [6], a fully convolutional network with star shape prior (FCN+SSP) [10], and a skin lesion segmentation deep model based on dilated residual and pyramid pooling network (SLSDeep) [9]. On the PH2 dataset, the competing methods consist of multi-stage FCN with parallel integration (mFCNPI) [7], a retrained FCN (RFCN) [4], and a simple linear iterative clustering (SLIC) method [11].…”

Section: Segmentation Resultsmentioning

confidence: 99%

“…A mass of automated skin lesion segmentation and classification methods have been proposed in the literature [4]- [29]. Among them, those based on deep convolutional neural networks (DCNNs) have achieved remarkable success [4], [6]- [10], [18]- [23], [28], [29], which are usually designed for either segmentation or classification task. However, skin lesion segmentation and classification are two highly related tasks.…”

Section: Introductionmentioning

confidence: 99%

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A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Xie

Zhang

Xia

et al. 2020

IEEE Trans. Med. Imaging

283

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Automated skin lesion segmentation and classification are two most essential and related tasks in the computeraided 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 enhancedSN, 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.

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Section: Segmentation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Xie

Zhang

Xia

et al. 2020

IEEE Trans. Med. Imaging

283

View full text Add to dashboard Cite

show abstract

“…In another related study of skin cancer, Mirikharaji et it. [176] also developed a deep FCN for skin lesion segmentation. They presented good results on ISBI 2017 dataset of dermoscopy images.…”

Section: Miscellaneousmentioning

confidence: 99%

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

et al. 2019

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Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in 'Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out 'lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.

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“…Mirikharaji and Hamarneh () introduced a star‐shape‐prior‐based technique for skin lesion segmentation that utilized Faster Convolutional Neural Networks (FCNN)s. The technique was based on semantic segmentation because Convolutional Neural Networks (CNN)s are the first choice for performing pixel‐wise lesion segmentation. The method was evaluated on the ISBI 2017 data set, and it achieved better accuracy among 21 participating groups.…”

Section: Related Workmentioning

confidence: 99%

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

Afza

Khan

Sharif

et al. 2019

Microscopy Res & Technique

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Among precision medical techniques, medical image processing is rapidly growing as a successful tool for cancer detection. Skin cancer is one of the crucial cancer types. It is identified through computer vision (CV) techniques using dermoscopic images. The early diagnosis skin cancer from dermoscopic images can be decrease the mortality rate. We propose an automated system for skin lesion detection and classification based on statistical normal distribution and optimal feature selection. Local contrast is controlled using a brighter channel enhancement technique, and segmentation is performed through a statistical normal distribution approach. The multiplication law of probability is implemented for the fusion of segmented images. In the feature extraction phase, optimized histogram, optimized color, and gray level co‐occurrences matrices features are extracted and covariance‐based fusion is performed. Subsequently, optimal features are selected through a binary grasshopper optimization algorithm. The selected optimal features are finally fed to a classifier and evaluated on the ISBI 2016 and ISBI 2017 data sets. Classification accuracy is computed using different Support Vector Machine (SVM) kernel functions, and the best accuracy is obtained for the cubic function. The average accuracies of the proposed segmentation on the PH2 and ISBI 2016 data sets are 93.79 and 96.04%, respectively, for an image size 512 × 512. The accuracies of the proposed classification on the ISBI 2016 and ISBI 2017 data sets are 93.80 and 93.70%, respectively. The proposed system outperforms existing methods on selected data sets.

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Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation

Cited by 98 publications

References 25 publications

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

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