Medical image segmentation has greatly increased healthcare sustainability. It is currently a major research area in the field of computer vision. The many artefacts inherent in medical images make it a difficult process to segment them. Deep neural models have recently demonstrated their use in a variety of image segmentation applications. With the rapid advancement of deep convolutional neural networks, medical image processing has become a study hotspot development of deep learning. The primary focus of this study is the deep learning-based segmentation of medical images.. This study gives an overview of the literature in the area of deep convolutional neural network-based medical image segmentation. The article looks at several popular medical image datasets, several segmentation task evaluation measures, and the effectiveness of various CNN-based networks. The current study also examines several issues in the area of segmenting medical images and various state-of-the-art solutions accessible in the literature, in contrast to the existing survey and review articles. The 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. Secondly, the paper describes the publicly available techniques of Network Training. Thirdly, it presents the various performance metrics employed for evaluating the deep learning segmentation models and the medical image segmentation  datasets. Finally, the paper also gives an insight into the major challenges faced in the field of image segmentation and their state-of-the-art solutions