A deep learning based review on abdominal images

Abstract

Computer-aided diagnosis have stumbled rapidly in the last few years. One of foremost step in computer-aided diagnosis is organ classification and segmentation. Among various organ segmentation techniques, the segmentation of abdominal organs like liver, stomach, kidney, pancreas and bladder from different modality of images has gotten keen interest in past few years. Mostly the interpretations of abdominal images are being done by medical experts or radiologists. Image interpretation by human experts is quite limited due to its subjectivity, complexity of the image, extensive variations exist across different interpreters, and fatigue. After the success of deep learning in real world applications, it is also providing exciting solutions with good accuracy for medical imaging and is seen as a key method for future applications in medical field. Emergence of deep Convolutional Neural Networks (CNN) tends to provide better classification in abdominal imaging analysis as compared to traditional models. This paper presents the state of the art of abdominal images for classifying abdominal organs based on deep learning and is a useful for computer-aided diagnosis applications. First this paper describe background of abdominal organs as well as modalities of imaging system. Then, we reviewed the techniques of deep learning for image segmentation, object detection, classification and other related tasks for multiorgan and single organ abdominal images. For single organ, different organs of abdomen such as liver, kidney, pancreas, and stomach are discussed seprately. In the last section, we have discussed current market challenges and the future recommendations.