Details

Autor(en) / Beteiligte

• Lu, Le
• Wang, Xiaosong
• Carneiro, Gustavo
• Yang, Lin

Titel

Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning

Ist Teil von

• Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, 2019, p.3-21

Ort / Verlag

Switzerland: Springer International Publishing AG

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Automatic pancreas segmentation in radiology Radiology image, e.g., computed tomography (CT), and magnetic resonance imaging (MRI)Magnetic Resonance Imaging (MRI), is frequently required by computer-aided Screening, diagnosis, and quantitative assessment. Yet, pancreas is a challenging abdominal Abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics. Recently, convolutional neural Convolutional Neural Networks (CNNs) (CNN) have demonstrated promising performance on accurate segmentation of pancreas. However, the CNN-based method often suffers from segmentation discontinuity for reasons such as noisy image quality and blurry pancreatic boundary. In this chapter, we first discuss the CNN configurations and training objectives that lead to the state-of-the-art performance on pancreas Pancreas segmentation. We then present a recurrent neural network (RNN)Recurrent Neural Network (RNN) to address the problem of segmentation spatial inconsistency across adjacent image slices. The Recurrent Neural Network (RNN) takes outputs of the CNN and refines the segmentation by improving the shape smoothness.