scholarly journals Pathological myopia classification with simultaneous lesion segmentation using deep learning

2021 ◽  
Vol 199 ◽  
pp. 105920
Author(s):  
Ruben Hemelings ◽  
Bart Elen ◽  
Matthew B. Blaschko ◽  
Julie Jacob ◽  
Ingeborg Stalmans ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lesion Segmentation ◽  
Pathological Myopia

Rethinking the Dice Loss for Deep Learning Lesion Segmentation in Medical Images

2021 ◽  
Vol 26 (1) ◽  
pp. 93-102
Author(s):  
Yue Zhang ◽  
Shijie Liu ◽  
Chunlai Li ◽  
Jianyu Wang
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
Lesion Segmentation

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials

Radiology ◽  
2021 ◽  
Author(s):  
Anitha Priya Krishnan ◽  
Zhuang Song ◽  
David Clayton ◽  
Laura Gaetano ◽  
Xiaoming Jia ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

scholarly journals Pathological Myopia Image Analysis Using Deep Learning

2019 ◽  
Vol 165 ◽  
pp. 239-244 ◽  
Author(s):  
Jaydeep Devda ◽  
R. Eswari
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Pathological Myopia

scholarly journals Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI

2020 ◽  
Vol 14 ◽  
Author(s):  
Chenyi Zeng ◽  
Lin Gu ◽  
Zhenzhong Liu ◽  
Shen Zhao
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Dice Similarity Coefficient ◽  
Learning Approaches ◽  
Review Of The Literature ◽  
Lesion Segmentation ◽  
Segmentation Methods ◽  
Future Direction

In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.

Skin lesion segmentation in clinical images using deep learning

2016 ◽  
Author(s):  
M.H. Jafari ◽  
N. Karimi ◽  
E. Nasr-Esfahani ◽  
S. Samavi ◽  
S.M.R. Soroushmehr ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Lesion Segmentation ◽  
Clinical Images

scholarly journals Lesion Segmentation in Breast Ultrasound Images using Optimized Marked Watershed Method

2021 ◽  
Author(s):  
Xiaoyan Shen ◽  
He Ma ◽  
Ruibo Liu ◽  
Hong Li ◽  
Jiachuan He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Stage ◽  
Speckle Noise ◽  
Histogram Equalization ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Lesion Segmentation ◽  
Early Screening ◽  
Side Window

Abstract Background: Breast cancer is one of the most serious diseases threatening women’s health. Early screening based on ultrasound can help to detect and treat tumors in early stage. However, due to the lack of radiologists with professional skills, ultrasound based breast cancer screening has not been widely used in rural area. Computer-aided diagnosis (CAD) technology can effectively alleviates this problem. Since Breast Ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD system, is challenging.Results: Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 BUS images from open source. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model, RDAU–NET. And its’ Accuracy(Acc), Dice efficient(DSC) and Jaccard Index(JI) reached 96.25%, 78.4% and 65.34% on dataset A, and ACC, DC and Sen reached 97.96%, 86.25% and 88.79% on dataset B.Conclusions: We proposed an adaptive morphology snake based on marked watershed(AMSMW) algorithm for BUS images segmentation. It was proven to be robust, efficient and effective. In addition, it was found to be more sensitive to malignant lesions than benign lesions. What’s more, since the Rectangular Region of Interest(RROI) in the proposed method is drawn manually, we will consider adding deep learning network to automatically identify RROI, and completely liberate the hands of radiologists.Methods: The proposed method consists of two main steps. In the first step, we used Contrast Limited Adaptive Histogram Equalization(CLAHE) and Side Window Filter(SWF) to preprocess BUS images. Therefore, lesion contours can be effectively highlighted and the influence of noise can be eliminated to a great extent. In the second step, we proposed adaptative morphology snake(AMS) as an embedded segmentation function of AMSMW. It can adjust working parameters adaptively, according to different lesions’ size. By combining segmentation results of AMS with marker region obtained by morphological method, we got the marker region of marked watershed (MW). Finally, we obtained candidate contours by MW. And the best lesion contour was chosen by maximum Average Radial Derivative(ARD).

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