Abstract
Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks. This significant growth is due to the achievements and high performance of the deep learning strategies. This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks. The paper examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks. In comparison to the existing review and survey papers, the present work also discusses the various challenges in the field of segmentation of medical images and different state-of-the-art solutions available in the literature.
Highlights
Image segmentation involves partitioning an input image into different segments with strong correlation with the region of interest (RoI) in the given image [1, 2]. e aim of medical image segmentation [3] is to represent a given input image in a meaningful form to study the anatomy, identify the region of interest (RoI), measure the volume of tissue to measure the size of tumor, and help in the deciding the dose of medicine, planning of treatment prior to applying radiation therapy, or calculating the radiation dose
All the aforementioned survey literatures discuss the various deep neural networks. is survey paper does focus on summarizing the different deep learning approaches and provides an insight into the different medical image datasets used for training deep neural networks and explains the metrics used for evaluating the performance of a model. e present work discusses the various challenges faced by DL based image segmentation models and their state-of-the-art solutions. e paper has several contributions which are as follows: Firstly, the present study provides an overview of the current state of the deep neural network structures utilized for medical image segmentation with their strengths and weaknesses
Intersection over union (IoU) or Jaccard index [82] is a metric commonly used for checking the performance of image segmentation algorithm
Summary
Deep neural network structures regional convolutional network deepLab model comparison, limitations, and advantages/Table 3. Application of deep neural network to Deep learning-based system literature review on DNN based image segmentation medical image segmentation models for different organs summary on deep learning-based medical image segmentation methods (Table 4). Medical image segmentation datasets Types and format of dataset different types of modalities summary of medical image segmentation datasets (Table 5)
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