Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published
