Adaptive median binary patterns for fully automatic nerves tracking in ultrasound images

Alkhatib, Mohammad; Hafiane, Adel; Tahri, Omar; Vieyres, Pierre; Delbos, Alain

doi:10.1016/j.cmpb.2018.03.013

Cited by 18 publications

(17 citation statements)

References 27 publications

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“…Thus, artificial intelligence-assisted ultrasound-guided target identification was used for the identification of the following anatomical structures (nerves): musculocutaneous, median, ulnar, and radial nerves, “interscalene-supraclavicular” and “infraclavicular brachial plexus,” “axillary level brachial plexus,” “erector spinae plane,” rectus sheath, “suprainguinal fascia iliaca,” adductor canal, “popliteal sciatic nerve,” “transverses abdominis plane,” anesthesia in the lower vertebrae regions (sacrum, intervertebral gaps, and vertebral bones), sciatic nerves, femoral nerve, subarachnoid and epidural spaces, facet blocks, navigation of blood vessels during UGRA ( 9 – 15 , 18 – 21 ) ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…The goal of the included studies was to accurately identify the target region (i.e., nerve block) on the ultrasound images in real-time (4). Therefore, some machine-learning methods have been proposed (Table 1) and their key techniques can be divided into (1) anatomic region segmentation, (2) target detection (i.e., feature extraction), and 3) tracking algorithm (9)(10)(11)(12)(13)(14)(15)(18)(19)(20)(21).…”

Section: Machine Learning Models and Algorithmsmentioning

confidence: 99%

“…Although the application of automated solutions has several benefits, the risks, failures, and limitations were also reported. Thus, the most important limitation was detection and tracking failure (if the nerve appearance is similar to surrounding areas), risk of the nerve disappearance and identical appearance with the surrounding areas-losing the nerve, issues with real-time tracking error after numerous iterations risk of failing to re-track lost nerve (9)(10)(11)(12)(13)(14)(15)(18)(19)(20)(21). Another limitation of this technology is the failure of distinguishing osseous images.…”

Section: Risks Failures and Limitations Of The Ai-assisted Ugramentioning

confidence: 99%

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Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

Viderman

Dossov²,

Seitenov³

2022

Front. Med.

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BackgroundRegional anesthesia is increasingly used in acute postoperative pain management. Ultrasound has been used to facilitate the performance of the regional block, increase the percentage of successfully performed procedures and reduce the complication rate. Artificial intelligence (AI) has been studied in many medical disciplines with achieving high success, especially in radiology. The purpose of this review was to review the evidence on the application of artificial intelligence for optimization and interpretation of the sonographic image, and visualization of needle advancement and injection of local anesthetic.MethodsTo conduct this scoping review, we followed the PRISMA-S guidelines. We included studies if they met the following criteria: (1) Application of Artificial intelligence-assisted in ultrasound-guided regional anesthesia; (2) Any human subject (of any age), object (manikin), or animal; (3) Study design: prospective, retrospective, RCTs; (4) Any method of regional anesthesia (epidural, spinal anesthesia, peripheral nerves); (5) Any anatomical localization of regional anesthesia (any nerve or plexus) (6) Any methods of artificial intelligence; (7) Settings: Any healthcare settings (Medical centers, hospitals, clinics, laboratories.ResultsThe systematic searches identified 78 citations. After the removal of the duplicates, 19 full-text articles were assessed; and 15 studies were eligible for inclusion in the review.ConclusionsAI solutions might be useful in anatomical landmark identification, reducing or even avoiding possible complications. AI-guided solutions can improve the optimization and interpretation of the sonographic image, visualization of needle advancement, and injection of local anesthetic. AI-guided solutions might improve the training process in UGRA. Although significant progress has been made in the application of AI-guided UGRA, randomized control trials are still missing.

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Section: Resultsmentioning

confidence: 99%

Section: Machine Learning Models and Algorithmsmentioning

confidence: 99%

Section: Risks Failures and Limitations Of The Ai-assisted Ugramentioning

confidence: 99%

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Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

Viderman

Dossov²,

Seitenov³

2022

Front. Med.

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“…In [10], the phase-based probabilistic gradient vector flow (PGVF) algorithm was used to track sciatic nerve region, obtaining an average Dice Similarity Coefficient (DSC) of 0.90. Alkhatib et al [2], instead, proposed the adaptive median binary pattern (AMBP) as the texture feature of a tracking algorithm with an accuracy of 95%. Hadjerci et al [9] proposed a segmentation pipeline including a pre-processing stage (filtering, de-noising, contrast enhancement), features extraction in a region of interest (ROI) and a support vector machine classifier.…”

Section: Related Workmentioning

confidence: 99%

A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet

Cosmo

Fiorentino

Villani

et al. 2022

Med Biol Eng Comput

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Ultrasound (US) imaging is recognized as a useful support for Carpal Tunnel Syndrome (CTS) assessment through the evaluation of median nerve morphology. However, US is still far to be systematically adopted to evaluate this common entrapment neuropathy, due to US intrinsic challenges, such as its operator dependency and the lack of standard protocols. To support sonographers, the present study proposes a fully-automatic deep learning approach to median nerve segmentation from US images. We collected and annotated a dataset of 246 images acquired in clinical practice involving 103 rheumatic patients, regardless of anatomical variants (bifid nerve, closed vessels). We developed a Mask R-CNN with two additional transposed layers at segmentation head to accurately segment the median nerve directly on transverse US images. We calculated the cross-sectional area (CSA) of the predicted median nerve. Proposed model achieved good performances both in median nerve detection and segmentation: Precision (Prec), Recall (Rec), Mean Average Precision (mAP) and Dice Similarity Coefficient (DSC) values are 0.916 ± 0.245, 0.938 ± 0.233, 0.936 ± 0.235 and 0.868 ± 0.201, respectively. The CSA values measured on true positive predictions were comparable with the sonographer manual measurements with a mean absolute error (MAE) of 0.918 mm2. Experimental results showed the potential of proposed model, which identified and segmented the median nerve section in normal anatomy images, while still struggling when dealing with infrequent anatomical variants. Future research will expand the dataset including a wider spectrum of normal anatomy and pathology to support sonographers in daily practice. Graphical abstract

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“…Thus, numerous studies of median nerve segmentation and tracking in ultrasonic images have been published. For example, Alkhatib et al (2018) proposed the adaptive median binary pattern (AMBP) as the texture feature of a tracking algorithm and compared it with the particle filter, mean shift and KanadeÀLucasÀTomasi methods; they found that the accuracy of AMBP was 95%. Hadjerci et al (2016a) proposed a segmentation system comprising pre-processing, feature extraction and support vector machine classifiers for selecting multiple targets of the median nerve, with a confidence decision that is ultimately used to determine the true object of the median nerve.…”

Section: Introductionmentioning

confidence: 99%

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

Horng

Yang

Sun

et al. 2020

Ultrasound in Medicine & Biology

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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 shortterm 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.

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Adaptive median binary patterns for fully automatic nerves tracking in ultrasound images

Cited by 18 publications

References 27 publications

Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet

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

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