Automated seeding for ultrasound skin lesion segmentation

Marosán, Péter; Szalai, Klára; Csabai, Domonkos; Csány, Gergely; Horváth, András; Gyöngy, Miklós

doi:10.1016/j.ultras.2020.106268

Cited by 11 publications

(26 citation statements)

References 23 publications

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“…Data were collected at the Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, Budapest, Hungary, as part of an ethically approved study. Informed consent was obtained from the participating patients for the anonymised use of the data for research and publication [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

“…Note that the same segmentation method was used for training and testing rather than selecting one as the ground truth during training. The first technique performs FA lesion segmentation based on an initial seeding step and a growing step, described in detail in [52], and described briefly below. The seeding step begins with a pre-processing substep to make ultrasound images from different machines similar to each other.…”

Section: Segmentationmentioning

confidence: 99%

“…The parameter set (Algorithm 1) and the entire workflow (Algorithm 2) of the current work is presented as an algorithm in pseudo-code form at the end of the section, focusing on the details of the classification method. Details of the FA segmentation algorithm are presented in Marosán et al [52], where figures of the procedure are also provided. Feature extraction is detailed in Table 2 and Section others'} mask L1 , mask L2 , ...mask LN // mask Lj is the lesion mask of the jth image mask D1 , mask D2 , ...mask DN // mask Dj is the dermis mask of the jth image F 1 , F 2 , ...F N // F j is the feature vector of the jth image…”

Section: List Of Symbols Avg-average Std-standard Deviation L-lesion Region (All the Pixels Inside The Lesion Mask) D-dermis Region (Pixementioning

confidence: 99%

“…The current work aims to present a framework for ultrasound-based skin cancer diagnosis that differentiates between three common skin lesion types: benign nevi, BCC and MM. In contrast to techniques that require some form of manual segmentation, the use of an automated segmentation method [ 52 ] makes skin cancer detection fully automatic, which, considering the time limits imposed on dermatological visits [ 53 ], would significantly improve the utility of the skin cancer detection method. Although many methods exist for optical-based automated skin cancer diagnosis [ 3 , 4 ], as mentioned earlier, ultrasound has the potential to improve on the accuracy of fully optical-based methods [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Automated Skin Lesion Classification on Ultrasound Images

et al. 2021

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The growing incidence of skin cancer makes computer-aided diagnosis tools for this group of diseases increasingly important. The use of ultrasound has the potential to complement information from optical dermoscopy. The current work presents a fully automatic classification framework utilizing fully-automated (FA) segmentation and compares it with classification using two semi-automated (SA) segmentation methods. Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained on 62 features, with ten-fold cross-validation. Six classification tasks were considered, namely all the possible permutations of one class versus one or two remaining classes. The receiver operating characteristic (ROC) area under the curve (AUC) as well as the accuracy (ACC) were measured. The best classification was obtained for the classification of nevi from cancerous lesions (melanoma, basal cell carcinoma), with AUCs of over 90% and ACCs of over 85% obtained with all segmentation methods. Previous works have either not implemented FA ultrasound-based skin cancer classification (making diagnosis more lengthy and operator-dependent), or are unclear in their classification results. Furthermore, the current work is the first to assess the effect of implementing FA instead of SA classification, with FA classification never degrading performance (in terms of AUC or ACC) by more than 5%.

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Section: Methodsmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

Section: List Of Symbols Avg-average Std-standard Deviation L-lesion Region (All the Pixels Inside The Lesion Mask) D-dermis Region (Pixementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Skin Lesion Classification on Ultrasound Images

et al. 2021

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“…The FCN's ability to use massive data sets to multilevel learn the features that best correlate to the image's presentation and semantics is mostly responsible for its popularity. Furthermore, FCN can be trained from end to end to effectively deduce pictures, i.e., as inputs, and the results are produced directly from segmentation [48][49]. As shown in Fig.…”

Section: Fcnmentioning

confidence: 99%

Skin Lesions Classification Using Deep Learning Techniques: Review

Kareem

Abdulazeez

Zeebaree

2021

AJRCoS

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Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

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