A Multi-Stage Framework With Context Information Fusion Structure For Skin Lesion Segmentation

Abstract

The computer-aided diagnosis (CAD) systems can highly improve the reliability and efficiency of melanoma recognition. As a crucial step of CAD, skin lesion segmentation has the unsatisfactory accuracy in existing methods due to large variability in lesion appearance and artifacts. In this work, we propose a framework employing multi-stage UNets (MS-UNet) in the auto-context scheme to segment skin lesion accurately end-to-end. We apply two approaches to boost the performance of MS-UNet. First, UNet is coupled with a context information fusion structure (CIFS) to integrate the low-level and context information in the multi-scale feature space. Second, to alleviate the gradient vanishing problem, we use deep supervision mechanism through supervising MS-UNet by minimizing a weighted Jaccard distance loss function. Four out of five commonly used performance metrics, including Jaccard index and Dice coefficient, show that our approach outperforms the state-ofthe-art deep learning based methods on the ISBI 2016 Skin Lesion Challenge dataset.

Automated Quantitative Analysis of Wound Histology Using Deep-Learning Neural Networks

“…The granulation tissue, epidermis, dermis, muscle, and background all were classified with accuracies !90%, whereas the scab and hair follicle classes were slightly lower with some misclassification along their boundaries with surrounding tissue regions (Figure 2b). Overall, the network had a classification accuracy of 92.5% when compared with the user-defined images in the test set, performing similarly to published segmentation networks for other applications (Calderon-Delgado et al, 2018;Oskal et al, 2019;Roy et al, 2017;Tang et al, 2019).…”

“…Recently, convolutional neural networks (CNNs) have been applied to many biomedical applications and demonstrated an ability to classify and segment large quantities of image data rapidly and accurately (Calderon-Delgado et al, 2018;Kose et al, 2020;Oskal et al, 2019;Rivenson et al, 2019;Ronneberger et al, 2015;Tang et al, 2019). CNNs typically utilize a deep-learning approach that allows them to learn features unique to different image regions and delineate them from other distinct regions of an image.…”

Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network

“…We conducted extensive comparisons of the proposed approach with ranked number one approaches [35] (in ISBI 2016 challenge) and other advanced methods on ISBI 2016 segmentation test set. these methods include the FCN method using Jaccard distance loss [20], the FCN method combining multi-scale input [21], the method of post-processing after segmentation [35], the method of hybrid FCN [38], the VGG-16 method combining hole convolution [39], the method using hybrid U-Net [41], multi-stage U-Net architecture [43], multiattention segmentation mechanism [44] and combine transform domain and CIELAB color space [45]. Table XI shows the final comparison results, it is seen in that our approach is better than the method [35], and the state-of-theart methods [44] in main indicators JA by margins of ∼2.4%, and ∼0.9%, respectively.…”

“…Li et al By constructing the residual network with different scale input and the calculation unit of the lesion index, the rough segmentation of lesion degree in the area of skin injury are realized [42]. Tang et al designed a multistage semantic segmentation model combined with context information to achieve the end-to-end accurate segmentation of skin lesion [43]. In order to improve the robustness and accuracy of lesion boundary segmentation, Xie.…”

Volumetric Segmentation and Characterisation of the Paracingulate Sulcus on MRI Scans

“…However, both methods require human participation, and the obtained results can be highly dependent on human experiences. Nowadays, deep learning based segmentation architectures have been applied on various types of biomedical images [9,10]. One of the pioneering models on semantic segmentation task is the fully convolutional neural network, which can predict the object class for each pixel [11].…”