Assessments of polymerization shrinkage by optical coherence tomography-based digital image correlation analysis-Part I: Parameter identification.

OBJECTIVE: To investigate the feasibility of optical coherence tomography (OCT)-based digital image correlation (DIC) analysis and to identify the experimental parameters for measurements of polymerization shrinkage. METHODS: Class I cavities were prepared on bovine incisors and filled with Filtek Z350XT Flowable (Z350F). One OCT image of the polymerized restoration was processed to generate virtually displaced images. In addition, the tooth specimen was physically moved under OCT scanning. A DIC software analyzed these virtual and physical transformation sets and assessed the effects of subset sizes on accuracy. The refractive index of unpolymerized and polymerized Z350F was measured via OCT images. Finally, different particles (70-80 µm glass beads, 150-212 µm glass beads, and 75-150 µm zirconia powder) were added to Z350F to inspect the analyzing quality. RESULTS: The analyses revealed a high correlation (>99.99%) for virtual movements within 131 pixels (639 µm) and low errors (<5.21%) within a 10-µm physical movement. A subset size of 51 × 51 pixels demonstrated the convergence of correlation coefficients and calculation time. The refractive index of Z350F did not change significantly after polymerization. Adding glass beads or zirconia particles caused light reflection or shielding in OCT images, whereas blank Z350F produced the best DIC analysis results. SIGNIFICANCE: The OCT-based DIC analysis with the experimental conditions is feasible in measuring polymerization shrinkage of RBC restorations. The subset size in the DIC analysis should be identified to optimize the analysis conditions and results. Uses of hyper- or hypo-reflective particles is not recommended in this method.

Validation of a Deep Learning-based Automatic Detection Algorithm for Measurement of Endotracheal Tube-to-Carina Distance on Chest Radiographs.

BACKGROUND: Improper endotracheal tube (ETT) positioning is frequently observed and potentially hazardous in the intensive care unit. The authors developed a deep learning-based automatic detection algorithm detecting the ETT tip and carina on portable supine chest radiographs to measure the ETT-carina distance. This study investigated the hypothesis that the algorithm might be more accurate than frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement. METHODS: A deep learning-based automatic detection algorithm was developed using 1,842 portable supine chest radiographs of 1,842 adult intubated patients, where two board-certified intensivists worked together to annotate the distal ETT end and tracheal bifurcation. The performance of the deep learning-based algorithm was assessed in 4-fold cross-validation (1,842 radiographs), external validation (216 radiographs), and an observer performance test (462 radiographs) involving 11 critical care clinicians. The performance metrics included the errors from the ground truth in ETT tip detection, carina detection, and ETT-carina distance measurement. RESULTS: During 4-fold cross-validation and external validation, the median errors (interquartile range) of the algorithm in ETT-carina distance measurement were 3.9 (1.8 to 7.1) mm and 4.2 (1.7 to 7.8) mm, respectively. During the observer performance test, the median errors (interquartile range) of the algorithm were 2.6 (1.6 to 4.8) mm, 3.6 (2.1 to 5.9) mm, and 4.0 (1.7 to 7.2) mm in ETT tip detection, carina detection, and ETT-carina distance measurement, significantly superior to that of 6, 10, and 7 clinicians (all P < 0.05), respectively. The algorithm outperformed 7, 3, and 0, 9, 6, and 4, and 5, 5, and 3 clinicians (all P < 0.005) regarding the proportions of chest radiographs within 5 mm, 10 mm, and 15 mm error in ETT tip detection, carina detection, and ETT-carina distance measurement, respectively. No clinician was significantly more accurate than the algorithm in any comparison. CONCLUSIONS: A deep learning-based algorithm can match or even outperform frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement.

Detecting Endotracheal Tube and Carina on Portable Supine Chest Radiographs Using One-Stage Detector with a Coarse-to-Fine Attention.

In intensive care units (ICUs), after endotracheal intubation, the position of the endotracheal tube (ETT) should be checked to avoid complications. The malposition can be detected by the distance between the ETT tip and the Carina (ETT-Carina distance). However, it struggles with a limited performance for two major problems, i.e., occlusion by external machine, and the posture and machine of taking chest radiographs. While previous studies addressed these problems, they always suffered from the requirements of manual intervention. Therefore, the purpose of this paper is to locate the ETT tip and the Carina more accurately for detecting the malposition without manual intervention. The proposed architecture is composed of FCOS: Fully Convolutional One-Stage Object Detection, an attention mechanism named Coarse-to-Fine Attention (CTFA), and a segmentation branch. Moreover, a post-process algorithm is adopted to select the final location of the ETT tip and the Carina. Three metrics were used to evaluate the performance of the proposed method. With the dataset provided by National Cheng Kung University Hospital, the accuracy of the malposition detected by the proposed method achieves 88.82% and the ETT-Carina distance errors are less than 5.333±6.240 mm.

Scaphoid Fracture Detection by Using Convolutional Neural Network.

Scaphoid fractures frequently appear in injury radiograph, but approximately 20% are occult. While there are few studies in the fracture detection of X-ray scaphoid images, their effectiveness is insignificant in detecting the scaphoid fractures. Traditional image processing technology had been applied to segment interesting areas of X-ray images, but it always suffered from the requirements of manual intervention and a large amount of computational time. To date, the models of convolutional neural networks have been widely applied to medical image recognition; thus, this study proposed a two-stage convolutional neural network to detect scaphoid fractures. In the first stage, the scaphoid bone is separated from the X-ray image using the Faster R-CNN network. The second stage uses the ResNet model as the backbone for feature extraction, and uses the feature pyramid network and the convolutional block attention module to develop the detection and classification models for scaphoid fractures. Various metrics such as recall, precision, sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) are used to evaluate our proposed method's performance. The scaphoid bone detection achieved an accuracy of 99.70%. The results of scaphoid fracture detection with the rotational bounding box revealed a recall of 0.789, precision of 0.894, accuracy of 0.853, sensitivity of 0.789, specificity of 0.90, and AUC of 0.920. The resulting scaphoid fracture classification had the following performances: recall of 0.735, precision of 0.898, accuracy of 0.829, sensitivity of 0.735, specificity of 0.920, and AUC of 0.917. According to the experimental results, we found that the proposed method can provide effective references for measuring scaphoid fractures. It has a high potential to consider the solution of detection of scaphoid fractures. In the future, the integration of images of the anterior-posterior and lateral views of each participant to develop more powerful convolutional neural networks for fracture detection by X-ray radiograph is probably important to research.

Computer-Assisted System in Stress Radiography for Anterior Cruciate Ligament Injury with Correspondent Evaluation of Relevant Diagnostic Factors.

We sought to design a computer-assisted system measuring the anterior tibial translation in stress radiography, evaluate its diagnostic performance for an anterior cruciate ligament (ACL) tear, and assess factors affecting the diagnostic accuracy. Retrospective research for patients with both knee stress radiography and magnetic resonance imaging (MRI) at our institution was performed. A complete ACL rupture was confirmed on an MRI. The anterior tibial translations with four different methods were measured in 249 patients by the designed algorithm. The diagnostic accuracy of each method in patients with all successful measurements was evaluated. Univariate logistic regression analysis for factors affecting diagnostic accuracy of method four was performed. In the inclusive 249 patients, 177 patients (129 with completely torn ACLs) were available for analysis. Mean anterior tibial translations were significantly increased in the patients with a completely torn ACL by all four methods, with diagnostic accuracies ranging from 66.7% to 75.1%. The diagnostic accuracy of method four was negatively associated with the time interval between stress radiography and MRI as well as force-joint distance on stress view, and not significantly associated with age, gender, flexion angle, intercondylar distance, and force-joint angle. A computer-assisted system measuring the anterior tibial translation in stress radiography showed acceptable diagnostic performance of complete ACL injury. A shorter time interval between stress radiography and MRI as well as shorter force-joint distance were associated with higher diagnostic accuracy.

Automatic Fetal Middle Sagittal Plane Detection in Ultrasound Using Generative Adversarial Network.

BACKGROUND AND OBJECTIVE: In the first trimester of pregnancy, fetal growth, and abnormalities can be assessed using the exact middle sagittal plane (MSP) of the fetus. However, the ultrasound (US) image quality and operator experience affect the accuracy. We present an automatic system that enables precise fetal MSP detection from three-dimensional (3D) US and provides an evaluation of its performance using a generative adversarial network (GAN) framework. METHOD: The neural network is designed as a filter and generates masks to obtain the MSP, learning the features and MSP location in 3D space. Using the proposed image analysis system, a seed point was obtained from 218 first-trimester fetal 3D US volumes using deep learning and the MSP was automatically extracted. RESULTS: The experimental results reveal the feasibility and excellent performance of the proposed approach between the automatically and manually detected MSPs. There was no significant difference between the semi-automatic and automatic systems. Further, the inference time in the automatic system was up to two times faster than the semi-automatic approach. CONCLUSION: The proposed system offers precise fetal MSP measurements. Therefore, this automatic fetal MSP detection and measurement approach is anticipated to be useful clinically. The proposed system can also be applied to other relevant clinical fields in the future.

Convolutional Neural Network-Based Humerus Segmentation and Application to Bone Mineral Density Estimation from Chest X-ray Images of Critical Infants.

Measuring bone mineral density (BMD) is important for surveying osteopenia in premature infants. However, the clinical availability of dual-energy X-ray absorptiometry (DEXA) for standard BMD measurement is very limited, and it is not a practical technique for critically premature infants. Developing alternative approaches for DEXA might improve clinical care for bone health. This study aimed to measure the BMD of premature infants via routine chest X-rays in the intensive care unit. A convolutional neural network (CNN) for humeral segmentation and quantification of BMD with calibration phantoms (QRM-DEXA) and soft tissue correction were developed. There were 210 X-rays of premature infants evaluated by this system, with an average Dice similarity coefficient value of 97.81% for humeral segmentation. The estimated humerus BMDs (g/cm3; mean ± standard) were 0.32 ± 0.06, 0.37 ± 0.06, and 0.32 ± 0.09, respectively, for the upper, middle, and bottom parts of the left humerus for the enrolled infants. To our knowledge, this is the first pilot study to apply a CNN model to humerus segmentation and to measure BMD in preterm infants. These preliminary results may accelerate the progress of BMD research in critical medicine and assist with nutritional care in premature infants.

DeepNerve: A New Convolutional Neural Network for the Localization and Segmentation of the Median Nerve in Ultrasound Image Sequences.

Carpal tunnel syndrome commonly occurs in individuals working in occupations that involve use of vibrating manual tools or tasks with highly repetitive and forceful manual exertion. In recent years, carpal tunnel syndrome has been evaluated by ultrasound imaging that monitors median nerve movement. Conventional image analysis methods, such as the active contour model, are typically used to expedite automatic segmentation of the median nerve, but these usually suffer from an arduous manual intervention. We propose a new convolutional neural network framework for localization and segmentation of the median nerve, called DeepNerve, that is based on the U-Net model. DeepNerve integrates the characteristics of MaskTrack and convolutional long short-term memory to effectively locate and segment the median nerve. On the basis of experimental results, the proposed model achieved high performance and generated average Dice measurement, precision, recall and F-score values of 0.8975, 0.8912, 0.9119 and 0.9015, respectively. The segmentation results of DeepNerve were significantly improved in comparison with those of conventional active contour models. Additionally, the results of Student's t-test revealed significant differences in four deformation measurements of the median nerve, including area, perimeter, aspect ratio and circularity. We conclude that the proposed DeepNerve not only generates satisfactory results for localization and segmentation of the median nerve, but also creates more promising measurements for applications in clinical carpal tunnel syndrome diagnosis.

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

BACKGROUND: Trigger finger is a common hand disease, which is caused by a mismatch in diameter between the tendon and the pulley. Ultrasound images are typically used to diagnose this disease, which are also used to guide surgical treatment. However, background noise and unclear tissue boundaries in the images increase the difficulty of the process. To overcome these problems, a computer-aided tool for the identification of finger tissue is needed. RESULTS: Two datasets were used for evaluation: one comprised different cases of individual images and another consisting of eight groups of continuous images. Regarding result similarity and contour smoothness, our proposed deeply supervised dilated fully convolutional DenseNet (D2FC-DN) is better than ATASM (the state-of-art segmentation method) and representative CNN methods. As a practical application, our proposed method can be used to build a tendon and synovial sheath model that can be used in a training system for ultrasound-guided trigger finger surgery. CONCLUSION: We proposed a D2FC-DN for finger tendon and synovial sheath segmentation in ultrasound images. The segmentation results were remarkably accurate for two datasets. It can be applied to assist the diagnosis of trigger finger by highlighting the tissues and generate models for surgical training systems in the future. METHODS: We propose a novel finger tendon segmentation method for use with ultrasound images that can also be used for synovial sheath segmentation that yields a more complete description for analysis. In this study, a hybrid of effective convolutional neural network techniques are applied, resulting in a deeply supervised dilated fully convolutional DenseNet (D2FC-DN), which displayed excellent segmentation performance on the tendon and synovial sheath.

Cerebral control of winking before and after learning: An event-related fMRI study.

INTRODUCTION: The main purpose of this study was to investigate the cerebral areas responsible for winking by observing the activation pattern and learning effects on cerebral cortices by comparing differences in activation pattern during winking before and after learning. METHODS: Sixty-three subjects were recruited, including 22 (11 males; 11 females) who could wink bilaterally and 41 (14 males; 27 females) who could wink unilaterally. Event-related functional magnetic resonance was performed. The subjects were asked to blink and wink according to projected instructions as the events for image analysis. The activation pattern was obtained by contrasting with the baseline images without eyelid movements. Those who could only wink unilaterally were asked to train themselves to wink the other eye. For those who succeeded (n = 24), another imaging study was performed and the results were compared with those before training. RESULTS AND CONCLUSION: Left winking resulted in activation in the left frontal lobe, while right winking resulted in activation in bilateral frontal lobes with predominance on the right side. For the subjects capable of only winking unilaterally, learning to wink on the other side activated similar cortical areas to those in the subjects capable of bilateral winking without training.

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network.

Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for assessing the curvature quantitatively is done by measuring the Cobb angle, which is the angle between two lines, drawn perpendicular to the upper endplate of the uppermost vertebra involved and the lower endplate of the lowest vertebra involved. However, manual measurement of spine curvature requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. In this article, we propose an automatic system for measuring spine curvature using the anterior-posterior (AP) view spinal X-ray images. Due to the characteristic of AP view images, we first reduced the image size and then used horizontal and vertical intensity projection histograms to define the region of interest of the spine which is then cropped for sequential processing. Next, the boundaries of the spine, the central spinal curve line, and the spine foreground are detected by using intensity and gradient information of the region of interest, and a progressive thresholding approach is then employed to detect the locations of the vertebrae. In order to reduce the influences of inconsistent intensity distribution of vertebrae in the spine AP image, we applied the deep learning convolutional neural network (CNN) approaches which include the U-Net, the Dense U-Net, and Residual U-Net, to segment the vertebrae. Finally, the segmentation results of the vertebrae are reconstructed into a complete segmented spine image, and the spine curvature is calculated based on the Cobb angle criterion. In the experiments, we showed the results for spine segmentation and spine curvature; the results were then compared to manual measurements by specialists. The segmentation results of the Residual U-Net were superior to the other two convolutional neural networks. The one-way ANOVA test also demonstrated that the three measurements including the manual records of two different physicians and our proposed measured record were not significantly different in terms of spine curvature measurement. Looking forward, the proposed system can be applied in clinical diagnosis to assist doctors for a better understanding of scoliosis severity and for clinical treatments.

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks.

Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy remains the primary diagnostic laboratory test. However, microscopic examination is always time-consuming and tedious. Therefore, an effective computer-aided image identification system is needed to provide timely assistance in diagnosis. The current identification system usually suffers from complex color variations of the images, resulting in plentiful of false object detection. To overcome the dilemma, we propose a two-stage Mycobacterium tuberculosis identification system, consisting of candidate detection and classification using convolution neural networks (CNNs). The refined Faster region-based CNN was used to distinguish candidates of M. tuberculosis and the actual ones were classified by utilizing CNN-based classifier. We first compared three different CNNs, including ensemble CNN, single-member CNN, and deep CNN. The experimental results showed that both ensemble and deep CNNs were on par with similar identification performance when analyzing more than 19,000 images. A much better recall value was achieved by using our proposed system in comparison with conventional pixel-based support vector machine method for M. tuberculosis bacilli detection.

Model-Based Orthodontic Assessments for Dental Panoramic Radiographs.

For better treatment outcomes, dentists usually use a set of parameters for orthodontic evaluation. In this study, a new method is proposed to assist dentists in obtaining reliable assessment of these parameters. The proposed method is based on dental panoramic radiographs and can be divided into four stages: image preprocessing, model training, tooth segmentation, and assessment of orthodontic parameters. The image is first normalized and enhanced. Then, the model training stage consists of shape and image model training, energy function training, and weight training. Next, we automatically segment the tooth contours in an energy-minimized manner. Finally, the automatic assessment of orthodontic parameters is carried out. The experimental results show that the average of absolute distance, the Dice similarity coefficient, and the average qualitative score ranged between 4.17 and 6.03, 0.87 and 0.90, as well as 2.58 and 3.12, respectively. The orthodontic assessment also is close to the evaluation of orthodontists. It has been shown that the proposed method can obtain accurate and consistent measurement in helping dentists to obtain an objective treatment evaluation.

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

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.

Characteristics of Sonography in a Rat Achilles Tendinopathy Model: Possible Non-invasive Predictors of Biomechanics.

The purpose of this study was to investigate the dynamic changes of histopathology, biomechanical properties, echo intensity, and ultrasound features in a collagenase-induced tendinopathy model of rat Achilles tendons, and to examine the associations among biomechanical properties, echo intensity, and ultrasound features. Forty-two rats received an ultrasound-guided collagenase injection on their left Achilles tendons, and needle puncture on the right ones as the control. At four, eight, and twelve weeks post-injury, the tendons were examined via measurements of their biomechanical properties, histopathological and ultrasonographic characteristics. The injured tendons showed significantly higher histopathological scores, lower Young's modulus, and higher ultrasound feature scores than the those of control ones throughout the study period. Up to week 12, all injured tendons showed defective healing. The neovascularization score had a significant negative linear association with the failure stress and Young's modulus. Maximum normalized echo intensity had a significant positive linear association with maximum strain. Therefore, neovascularization and maximum normalized echo intensity are associated with mechanically altered tendinopathic tendons. Non-invasive ultrasound methodology, including echo intensity and ultrasound feature scores, may provide useful information about biomechanical properties of tendinopathic tendons.

Tendon-motion tracking in an ultrasound image sequence using optical-flow-based block matching.

BACKGROUND: Tendon motion, which is commonly observed using ultrasound imaging, is one of the most important features used in tendinopathy diagnosis. However, speckle noise and out-of-plane issues make the tracking process difficult. Manual tracking is usually time consuming and often yields inconsistent results between users. METHODS: To automatically track tendon motion in ultrasound images, we developed a new method that combines the advantages of optical flow and multi-kernel block matching. For every pair of adjacent image frames, the optical flow is computed and used to estimate the accumulated displacement. The proposed method selects the frame interval adaptively based on this displacement. Multi-kernel block matching is then computed on the two selected frames, and, to reduce tracking errors, the detailed displacements of the frames in between are interpolated based on the optical flow results. RESULTS: In the experiments, cadaver data were used to evaluate the tracking results. The mean absolute error was less than 0.05 mm. The proposed method also tracked the motion of tendons in vivo, which provides useful information for clinical diagnosis. CONCLUSION: The proposed method provides a new index for adaptively determining the frame interval. Compared with other methods, the proposed method yields tracking results that are significantly more accurate.

Dielectrophoresis System for Testing Antimicrobial Susceptibility of Gram-Negative Bacteria to ß-Lactam Antibiotics.

Gram-negative bacteria (GNBs) are common pathogens causing severe sepsis. Rapid evaluation of drug susceptibility would guide effective antibiotic treatment and promote life-saving. A total of 78 clinical isolates of 13 Gram-negative species collected between April 2013 and November 2013 from two medical centers in Tainan were tested. Bacterial morphology changes in different concentrations of antibiotics were observed under the electric field of a quadruple electrode array using light microscopy. The minimal inhibitory concentrations (MICs) of four antimicrobial agents, namely, cefazolin, ceftazidime, cefepime, and doripenem, were determined by the dielectrophoretic antimicrobial susceptibility testing (dAST) and by the conventional broth dilution testing (BDT). The antibiotics at the concentration of 1× MIC induced obvious morphological changes in susceptible GNBs, including cell elongation, cell swelling, or lysis, at 90 min. In contrast, resistant strains remained unchanged. The MIC results measured by dAST were in good agreement with those of BDT (essential agreement 95.6%). The category agreement rate was 89.2%, and the very major errors rate for dAST was 2.9%. In conclusion, dAST could accurately determine drug susceptibility within 90 min. Comprehensive tests by dAST for more drugs against more GNB species are possible in the future.

Identification of the Position and Thickness of the First Annular Pulley in Sonographic Images.

The purpose was to identify the A1 pulley's exact location and thickness by comparing measurements from a clinical high-frequency ultrasound scanner system (CHUS), a customized high-frequency ultrasound imaging research system (HURS) and a digital caliper. Ten cadaveric hands were used. We explored the pulley by layers, inserted guide pins and scanned it with the CHUS. After identifying the pulley, we measured each long finger's thickness using the CHUS and excised the pulley to measure its thickness with a digital caliper and the HURS. The thin hypo-echoic layer was revealed to be the synovial fluid space, and the pulley appears hyper-echoic regardless of scan direction. We also defined the pulley's boundaries. Moreover, the CHUS provided a significantly lower measurement of the pulley's thickness than the digital caliper and HURS. Likewise, based on the digital caliper's measurement, the HURS had significantly lower mean absolute and relative errors than the CHUS.

Effects of different extents of pulley release on tendon excursion efficiency and tendon moment arms.

To compare the excursion efficiency and moment arms of flexor digitorum superficialis (FDS) and profundus (FDP) among different conditions of pulley integrity related to trigger finger treatment, cadaveric fingers were first tested with an intact pulley system, and then the first (A1) and second (A2) annular pulleys were released gradually from the proximal to distal part. Linear position sensors and a motion capture system were used to measure the tendon excursion and joint rotation simultaneously. The tendon excursion efficiency was defined as the range of motion of the involved joints per unit of tendon excursion, and the tendon moment arm was determined by the slope of the linear fitting result of tendon excursion versus metacarpophalangeal (MCP) joint rotation. No significant differences were found between the release of the A1 pulley and the release extending to half the proximal part of the A2 pulley in the FDP excursion efficiency and the moment arms of FDS and FDP with respect to the MCP joint. These results imply that the release could extend to half the proximal A2 pulley, if necessary, without significantly decreasing the FDP excursion efficiency and increasing the moment arms of FDS and FDP with respect to the MCP joint.

Finger movement function after ultrasound-guided percutaneous pulley release for trigger finger: effects of postoperative rehabilitation.

OBJECTIVE: To develop and test a postoperative rehabilitation protocol for use by individuals with trigger finger undergoing ultrasound-guided percutaneous pulley release. DESIGN: Nonrandomized controlled trial. SETTING: Hospital and local community. PARTICIPANTS: Individuals suffering from trigger finger with joint contracture (N=21) were recruited and grouped into an intervention group (n=9) or a control group (n=12). INTERVENTIONS: All the participants underwent the same surgical procedure performed by the same surgeon. A 4-week postoperative rehabilitation program was designed based on the wound healing process. The intervention group received postoperative rehabilitation after the surgery, whereas the control group received no treatment after the surgery. MAIN OUTCOME MEASURES: The finger movement functions were quantitatively evaluated before and 1 month after the surgery using a 3-dimensional motion capture system. The fingertip workspace and joint range of motion (ROM) were evaluated while the participant was performing a sequential 5-posture movement, including finger extension, intrinsic plus, straight fist, full fist, and hook fist. RESULTS: The intervention group demonstrated significantly more improvements than the control group in the fingertip workspace (49% vs 17%), ROM of the distal interphalangeal (DIP) joint (16% vs 4%), ROM of the proximal interphalangeal (PIP) joint (21% vs 5%), and total active ROM (17% vs 5%). CONCLUSIONS: This pilot study evaluated a postoperative rehabilitation protocol for trigger finger and demonstrated its effects on various finger functions. Participants who underwent the rehabilitation program had significantly more improvements in the fingertip workspace, ROM of the DIP and PIP joints, and total active ROM.