A dual path encoder-decoder network for placental vessel segmentation in fetoscopic surgery

• Yunbo Rao,
• Tian Tan,
• Shaoning Zeng,
• Zhanglin Chen,
• Jihong Sun,

Vol. 18, No. 1, January 31, 2024
10.3837/tiis.2024.01.002, Download Paper (Free):

• Fetoscopy
• Deep Learning
• Vessel Segmentation
• Twin-to-Twin Transfusion Syndrome

Abstract

A fetoscope is an optical endoscope, which is often applied in fetoscopic laser photocoagulation to treat twin-to-twin transfusion syndrome. In an operation, the clinician needs to observe the abnormal placental vessels through the endoscope, so as to guide the operation. However, low-quality imaging and narrow field of view of the fetoscope increase the difficulty of the operation. Introducing an accurate placental vessel segmentation of fetoscopic images can assist the fetoscopic laser photocoagulation and help identify the abnormal vessels. This study proposes a method to solve the above problems. A novel encoder-decoder network with a dual-path structure is proposed to segment the placental vessels in fetoscopic images. In particular, we introduce a channel attention mechanism and a continuous convolution structure to obtain multi-scale features with their weights. Moreover, a switching connection is inserted between the corresponding blocks of the two paths to strengthen their relationship. According to the results of a set of blood vessel segmentation experiments conducted on a public fetoscopic image dataset, our method has achieved higher scores than the current mainstream segmentation methods, raising the dice similarity coefficient, intersection over union, and pixel accuracy by 5.80%, 8.39% and 0.62%, respectively.

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