A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images

Abstract

Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint’s tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians’ opinions.

Segmentation of finger tendon and synovial sheath in ultrasound image using deep convolutional neural network

“…Two datasets of ultrasound images of fingers at the A1 pulley, captured in the transverse view, were used in this study. The first set was the Chuang's dataset [16], which was used to make comparisons with the proposed method. The second set consisted of eight groups of images captured for building a model of finger tissue for a surgical training system, termed the modeling-building (MB) dataset.…”

“…The ASM and ACM are the most widely used methods for soft tissue segmentation. In our previous work [16], we proposed an adaptive texture-based ASM (ATASM) for segmentation of the tendon and synovium sheath. The ATASM applies a shape model together with texture feature generation and then uses a genetic algorithm-based energy optimization.…”

“…This paper is organized as follows. The data material of this study and experimental results are described in "Results" and "Discussion" sections that demonstrate Chuang's dataset [16] with independent images that were used to compare the results of this study with conventional methods, and a dataset with eight groups of images used for model building. Conclusions of this study are presented subsequently.…”

Comparison of Sonographic Evaluation of the Median Nerve and Fourth Flexor Tendon Between Asymptomatic Subjects With and Without Diabetes

“…It can reveal median nerve enlargement at the proximal wrist, median nerve compression during stress testing, and muscle intrusion during hand motion (digit flexion or extension) that compresses the median nerve [7,10]. Stenosing flexor tenosynovitis, or trigger finger of the thumb and fingers, is a common cause of hand pain [11][12][13][14]. It seems to be caused by the thickening and narrowing of annular pulleys or tendon abnormalities which result in pain, clicking, snapping, or locking of the affected finger.…”

“…A diagnosis of trigger finger is based on symptoms and physical examination [12,13]. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity [11]. Changes in the cross-sectional area of flexor tendons of the hand may precede clinical tendinopathy in diabetic patients.…”