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Review
. 2021 Aug;11(8):3830-3853.
doi: 10.21037/qims-20-1151.

What is new in computer vision and artificial intelligence in medical image analysis applications

Jimena Olveres  1   2 , Germán González  2 , Fabian Torres  1   2 , José Carlos Moreno-Tagle  2 , Erik Carbajal-Degante  3 , Alejandro Valencia-Rodríguez  4 , Nahum Méndez-Sánchez  4   5 , Boris Escalante-Ramírez  1   2
Affiliations
Review

What is new in computer vision and artificial intelligence in medical image analysis applications

Jimena Olveres et al. Quant Imaging Med Surg. 2021 Aug.

Abstract

Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis.

Keywords: Artificial intelligence (AI); cardiology; computer vision (CV); gastroenterology; medical image analysis; microscopy; neurodegenerative disorders; oncology; respiratory diseases.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-1151). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Relation between computer vision and artificial intelligence.
Figure 2
Figure 2
The flow of computer vision and machine learning interaction in medical imaging
Figure 3
Figure 3
Modules of a segmentation step using UNet deep learning as initialization.
Figure 4
Figure 4
Motion estimation. (A) 2D motion estimation results for heart left ventricle on systole, short axis view. (B) Heart volume with 3D motion estimation results on diastole.
Figure 5
Figure 5
Detection, segmentation, and classification of breast lesions in US using Mask R-CNN. (A) Malignant lesion. (B) Benign lesion.
Figure 6
Figure 6
Simplified architecture of a Convolutional Neural Network.
Figure 7
Figure 7
Processed test images by the trained deep neural network. Nevi correctly detected as benign and melanoma correctly detected as malignant (upper row). Seborrheic keratosis (benign lesion) and melanoma correctly detected (lower row).
Figure 8
Figure 8
General scheme of classification based on discrete orthogonal moments. After computing local DOMs using sliding windows, the statistical textural feature vectors are computed and classified.
Figure 9
Figure 9
Segmentation results of midbrain in MRI by Olveres et al. (99).
Figure 10
Figure 10
AI-based online tool that predicts COVID-19 and segments (green and red contours) lung lesions associated to the disease (https://www.imagensalud.unam.mx).
See this image and copyright information in PMC

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