Deep Learning in Image Processing: Part 2—Image Enhancement, Reconstruction and Registration

TY - CHAP

T1 - Deep Learning in Image Processing

T2 - Part 2—Image Enhancement, Reconstruction and Registration

AU - Pauwels, Ruben

AU - Iosifidis, Alexandros

PY - 2024/1

Y1 - 2024/1

N2 - The digital dental workflow involves the use of several types of images, including two-dimensional (radiographs, photographs), 3D tomographic (computed tomography, cone-beam computed tomography, magnetic resonance imaging, ultrasound), and 3D mesh-type (intra-oral scans, facial scans) data. While the combined use of these various imaging modalities augments the diagnostic and treatment process, it can be challenging to make optimal use of them. Deep learning (DL) has shown tremendous potential for image processing tasks and is progressively being integrated within the clinical workflow. The previous chapter described neural networks with particular use in image processing, as well as applications in image segmentation. This chapter looks at applications of DL in image enhancement, image reconstruction and image registration. Some of the applications in this chapter are somewhat more exploratory, in the sense that higher-level diagnostic efficacy remains to be assessed. This field is expected to continue evolving in the following years; whether the optimal approach is to have DL methods operate fully independently or in combination with existing image processing techniques remains to be seen.

AB - The digital dental workflow involves the use of several types of images, including two-dimensional (radiographs, photographs), 3D tomographic (computed tomography, cone-beam computed tomography, magnetic resonance imaging, ultrasound), and 3D mesh-type (intra-oral scans, facial scans) data. While the combined use of these various imaging modalities augments the diagnostic and treatment process, it can be challenging to make optimal use of them. Deep learning (DL) has shown tremendous potential for image processing tasks and is progressively being integrated within the clinical workflow. The previous chapter described neural networks with particular use in image processing, as well as applications in image segmentation. This chapter looks at applications of DL in image enhancement, image reconstruction and image registration. Some of the applications in this chapter are somewhat more exploratory, in the sense that higher-level diagnostic efficacy remains to be assessed. This field is expected to continue evolving in the following years; whether the optimal approach is to have DL methods operate fully independently or in combination with existing image processing techniques remains to be seen.

U2 - 10.1007/978-3-031-43827-1_18

DO - 10.1007/978-3-031-43827-1_18

M3 - Book chapter

SN - 978-3-031-43826-4

SN - 978-3-031-43829-5

SP - 317

EP - 351

BT - Artificial Intelligence in Dentistry

A2 - Orhan, K.

A2 - Jagtap, R.

PB - Springer

CY - Cham

ER -