scholarly journals Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1964
Author(s):  
Reza Kalantar ◽  
Gigin Lin ◽  
Jessica M. Winfield ◽  
Christina Messiou ◽  
Susan Lalondrelle ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Technological Development ◽  
Automatic Segmentation ◽  
Research Attention ◽  
Segmentation Algorithms ◽  
Rectal Cancers ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.

scholarly journals Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

2021 ◽  
Author(s):  
Reza Kalantar ◽  
Gigin Lin ◽  
Jessica M Winfield ◽  
Christina Messiou ◽  
Susan Lalondrelle ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Technology Development ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Large Datasets ◽  
Research Attention ◽  
Segmentation Algorithms ◽  
Rectal Cancers ◽  
Aided Diagnosis

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides ground for technology development for computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive and clinically-oriented overview of DL-based segmentation studies for bladder, prostate, cervical and rectal cancers, highlighting the key findings, challenges and limitations.

scholarly journals 3D U-Net for Skull Stripping in Brain MRI

2019 ◽  
Vol 9 (3) ◽  
pp. 569 ◽  
Author(s):  
Hyunho Hwang ◽  
Hafiz Zia Ur Rehman ◽  
Sungon Lee
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Extraction ◽  
Skull Stripping ◽  
Segmentation Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Network ◽  
The Brain

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

scholarly journals SKIN LESION CLASSIFICATION FROM DERMOSCOPY AND CLINICAL IMAGES WITH A DEEP LEARNING APPROACH

2021 ◽  
Vol 9 (10) ◽  
pp. 1294-1300
Author(s):  
Aigli Korfiati ◽  
◽  
Giorgos Livanos ◽  
Christos Konstandinou ◽  
Sophia Georgiou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Medical Image ◽  
Skin Lesions ◽  
Learning Approaches ◽  
Early Screening ◽  
Clinical Images ◽  
Cad Systems ◽  
Learning Classifier ◽  
Aided Diagnosis

Computer-aided diagnosis (CAD) systems based on deep learning approaches are now feasible due to the availability of big data and the availability of powerful computational resources.The medical image-based CAD systems are of great interest in numerous diseases, but especially for skin cancer diagnosis, deep learning models have been mostly developed for dermoscopy images. Models for clinical images are few, mainly due to the unavailability of big volumes of relevant data. However, CAD systems able to classify skin lesions from clinical images would be of great valueboth for the population and clinicians as an initial early screening of lesions that would leadpatients to visiting a dermatologist in case of suspicious lesions. This is even more pronounced in areas where there is lack of dermoscopy instruments. Thus, in this paper, we aimed to build a classifier based on bothdermoscopy and clinical images able to discriminate skin cancer from skin lesions. The classification is made among three benign and two malignant categories, which include Nevus, Benign but not nevus, Benign but suspicious for malignancy, Melanoma and Non-Melanocytic Carcinoma.The proposed deep learning classifier achieves an Area Under Curve ranging between 0.75 and 0.9 for the five examined categories.

Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method

2011 ◽  
Vol 58-60 ◽  
pp. 2370-2375
Author(s):  
Wei Li Ding ◽  
Feng Jiang ◽  
Jia Qing Yan
Keyword(s):  
Level Set ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Image Sequences ◽  
Initial Contour ◽  
Single Image ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Image

Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

scholarly journals Brain Structures Segmentation by using Statistical Models

2017 ◽  
Vol 1 (3) ◽  
pp. 59
Author(s):  
Abdelfettah Meziane ◽  
Saïd MAHMOUDI ◽  
Mohammed Amine CHIKH
Keyword(s):  
Automatic Segmentation ◽  
Brain Structures ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Active Appearance Models ◽  
Active Shape Models ◽  
Shape Models ◽  
Appearance Models ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

Automatic segmentation of brain structures is a fundamental step for quantitative analysis of images in many brain’s pathologies such as Alzheimer’s, brain’s tumors or multiple sclerosis. The large variation of brain structures requires the development of efficient and specific methods, often by using Magnetic Resonance Imaging (MRI) modality. The goal of our work is to implement an automatic brain’s structures segmentation method that uses the active shape models (ASM) and active appearance models (AAM) techniques. Another goal of this work is to compare the performances of these segmentation approaches, and also to evaluate their use in a computer aided diagnosis tools and to compare their performances.

Directional Vector-Based Skin Lesion Segmentation — A Novel Approach to Skin Segmentation

2020 ◽  
Vol 20 (03) ◽  
pp. 2050021
Author(s):  
P. Nikesh ◽  
G. Raju
Keyword(s):  
Skin Lesion ◽  
Automatic Segmentation ◽  
High Accuracy ◽  
Lesion Segmentation ◽  
Seed Point ◽  
Skin Segmentation ◽  
Novel Approach ◽  
Segmentation Algorithms ◽  
Computer Aided ◽  
Aided Diagnosis

Efficient skin lesion segmentation algorithms are required for computer aided diagnosis of skin cancer. Several algorithms were proposed for skin lesion segmentation. The existing algorithms are short of achieving ideal performance. In this paper, a novel semi-automatic segmentation algorithm is proposed. The fare concept of the proposed is 8-directional search based on threshold for lesion pixel, starting from a user provided seed point. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. In comparison to a chosen set of algorithms, the proposed approach gives high accuracy and specificity values. A significant advantage of the proposed method is the ability to deal with discontinuities in the lesion.

Deep Learning for Medical Image Segmentation

2021 ◽  
pp. 40-77
Author(s):  
Kanchan Sarkar ◽  
Bohang Li
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Disease Diagnosis ◽  
Medical Image Segmentation ◽  
Kidney Tumour ◽  
Tumour Segmentation ◽  
Recent Innovation ◽  
Segmentation Algorithms

Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging applications. Various segmentation algorithms have been studied and applied in medical imaging for many years, but the problem remains challenging due to growing a large number of variety of applications starting from lung disease diagnosis based on x-ray images, nucleus detection, and segmentation based on microscopic pictures to kidney tumour segmentation. The recent innovation in deep learning brought revolutionary advances in computer vision. Image segmentation is one such area where deep learning shows its capacity and improves the performance by a larger margin than its successor. This chapter overviews the most popular deep learning-based image segmentation techniques and discusses their capabilities and basic advantages and limitations in the domain of medical imaging.

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