This chapter presents examples of medical image understanding algorithms using computational anatomy models explained in Chap. 2. After the introductory in Sect. 3.1, Sect. 3.2 shows segmentation algorithms for vertebrae, ribs, and hip joints. Segmentation algorithms for skeletal muscle and detection algorithms for lymph nodes are explained in Sects. 3.3 and 3.4, respectively. Section 3.5 deals with algorithms for understanding organs/tissues in the head and neck regions and starts with computational neuroanatomy, followed by analysis and segmentation algorithms for white matter, brain CT, oral regions, fundus oculi, and retinal optical coherence tomography (OCT). Algorithms useful in the thorax, specifically for the lungs, tracheobronchial tree, vessels, and interlobar fissures from a thoracic CT volume, are presented in Sect. 3.6. Section 3.7 provides algorithms for breast ultrasound imaging, i.e., mammography and breastMRI. Cardiac imaging algorithms in an echocardiographic image sequence and MR images as well as coronary arteries in a CT volume are explained in Sect. 3.8. Section 3.9 deals with segmentation algorithms of abdominal organs, including the liver, pancreas, spleen, kidneys, gastrointestinal tract, and abdominal blood vessels, followed by anatomical labeling of segmented vessels.
|Title of host publication||Computational Anatomy Based on Whole Body Imaging|
|Subtitle of host publication||Basic Principles of Computer-Assisted Diagnosis and Therapy|
|Number of pages||134|
|Publication status||Published - 2017 Jun 14|
- Fundus image