Skin lesion boundary segmentation with fully automated deep extreme cut methods

The skin is the largest organ in our body. There is a high prevalence of skin diseases and a scarcity of dermatologists, the experts in diagnosing and managing skin diseases, making CAD (Computer Aided Diagnosis) of skin disease an important field of research. Many patients present with a skin lesion of concern, to determine if it is benign or malignant. Lesion diagnosis is currently performed by dermatologists taking a history and examining the lesion and the entire body surface with the aid of a dermatoscope. Automatic lesion segmentation and evaluation of the symmetry or asymmetry of structures and colors with the help of computers may classify a lesion as likely benign or as likely malignant. We have explored a deep learning program called Deep Extreme Cut (DEXTR) and used the Faster-RCNN-InceptionV2 network to determine extreme points (left-most, right-most, top and bottom pixels). We used the ISIC challenge-2017 images for the training set and received Jaccard index of 82.2% on the ISIC testing set 2017 and 85.8% on the PH2 dataset. The proposed method outperformed the winner algorithm of the competition by 5.7% for the Jaccard index.

[1]  Luc Van Gool,et al.  Deep Extreme Cut: From Extreme Points to Object Segmentation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  Manu Goyal,et al.  Breast ultrasound lesions recognition: end-to-end deep learning approaches , 2018, Journal of medical imaging.

[3]  Manu Goyal,et al.  End-to-end breast ultrasound lesions recognition with a deep learning approach , 2018, Medical Imaging.

[4]  Nicola Sverzellati,et al.  Late Breaking Abstract - A Deep Learning Algorithm for Classifying Fibrotic Lung Disease on High Resolution Computed Tomography , 2018, Idiopathic interstitial pneumonias.

[5]  Neil D. Reeves,et al.  DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[6]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[7]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling , 2015, CVPR 2015.

[8]  Manu Goyal,et al.  Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images , 2018, ArXiv.

[9]  Noel C. F. Codella,et al.  Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[10]  João Paulo Papa,et al.  Computational methods for the image segmentation of pigmented skin lesions: A review , 2016, Comput. Methods Programs Biomed..

[11]  Rafael García,et al.  Computerized analysis of pigmented skin lesions: A review , 2012, Artif. Intell. Medicine.

[12]  Manu Goyal,et al.  Facial Skin Classification Using Convolutional Neural Networks , 2017, ICIAR.

[13]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[14]  Manu Goyal,et al.  Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks , 2017, BIOINFORMATICS.

[15]  Antonio Criminisi,et al.  Segmentation of Brain Tumor Tissues with Convolutional Neural Networks , 2014 .

[16]  Neil D. Reeves,et al.  Fully convolutional networks for diabetic foot ulcer segmentation , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[17]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  David I. McLean,et al.  Generalizing Common Tasks in Automated Skin Lesion Diagnosis , 2011, IEEE Transactions on Information Technology in Biomedicine.

[21]  Manu Goyal,et al.  Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices , 2019, IEEE Journal of Biomedical and Health Informatics.

[22]  Manu Goyal,et al.  Region of Interest Detection in Dermoscopic Images for Natural Data-augmentation , 2018, ArXiv.

[23]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[24]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[25]  Mun-Taek Choi,et al.  Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks , 2018, Comput. Methods Programs Biomed..

[26]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[27]  M. Mohammed Thaha,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2019, Journal of Medical Systems.

[28]  Graham D. Finlayson,et al.  Shades of Gray and Colour Constancy , 2004, CIC.

[29]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Manu Goyal,et al.  Multi-Class Lesion Diagnosis with Pixel-wise Classification Network , 2018, ArXiv.