A Study on Using Deep Learning for Segmentation of Medical Image

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.

Download Full-text

Medical Image Processing and Deep Learning to Diagnose COVID-19 with CT Images

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA) ◽

10.1109/ipria53572.2021.9483563 ◽

2021 ◽

Author(s):

Abbas Mazrouei Sebdani ◽

Amir Mostafavi

Keyword(s):

Image Processing ◽

Deep Learning ◽

Medical Image ◽

Medical Image Processing ◽

Ct Images

Download Full-text

Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment

Journal of Electrical and Computer Engineering ◽

10.1155/2018/2187247 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Chen Zhao ◽

Jungang Han ◽

Yang Jia ◽

Lianghui Fan ◽

Fan Gou

Keyword(s):

Image Processing ◽

Deep Learning ◽

Medical Image ◽

Deep Neural Networks ◽

Medical Image Processing ◽

Bone Age ◽

Age Assessment ◽

Image Processing And Analysis ◽

Learning Technique ◽

Bone Age Assessment

Deep learning technique has made a tremendous impact on medical image processing and analysis. Typically, the procedure of medical image processing and analysis via deep learning technique includes image segmentation, image enhancement, and classification or regression. A challenge for supervised deep learning frequently mentioned is the lack of annotated training data. In this paper, we aim to address the problems of training transferred deep neural networks with limited amount of annotated data. We proposed a versatile framework for medical image processing and analysis via deep active learning technique. The framework includes (1) applying deep active learning approach to segment specific regions of interest (RoIs) from raw medical image by using annotated data as few as possible; (2) generative adversarial Network is employed to enhance contrast, sharpness, and brightness of segmented RoIs; (3) Paced Transfer Learning (PTL) strategy which means fine-tuning layers in deep neural networks from top to bottom step by step to perform medical image classification or regression tasks. In addition, in order to understand the necessity of deep-learning-based medical image processing tasks and provide clues for clinical usage, class active map (CAM) is employed in our framework to visualize the feature maps. To illustrate the effectiveness of the proposed framework, we apply our framework to the bone age assessment (BAA) task using RSNA dataset and achieve the state-of-the-art performance. Experimental results indicate that the proposed framework can be effectively applied to medical image analysis task.

Download Full-text