A Novel Automatic Morphologic Analysis of Eyelids Based on Deep Learning Methods

Cao, Jing; Lou, Lixia; You, Kun; Gao, Zhiyuan; Jin, Kai; Shao, Jing; Ye, Juan

doi:10.1080/02713683.2021.1908569

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…Some researchers have attempted to quantitatively describe the eyelid contour on digital face images (11)(12)(13)(14)(15)(16)31,32,(37)(38)(39)(40)(41)(42). Cruz et al (37) measured the upper eyelid contour in ptosis and Graves disease and fitted the contours with second-degree polynomial functions.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

Shao¹,

Huang²,

Gao³

et al. 2023

Quant Imaging Med Surg

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Background:We aimed to propose a deep learning-based approach to automatically measure eyelid morphology in patients with thyroid-associated ophthalmopathy (TAO).Methods: This prospective study consecutively included 74 eyes of patients with TAO and 74 eyes of healthy volunteers visiting the ophthalmology department in a tertiary A hospital. Patients diagnosed as TAO and healthy volunteers who were age-and gender-matched met the eligibility criteria for recruitment.Facial images were taken under the same light conditions. Comprehensive eyelid morphological parameters, such as palpebral fissure (PF) length, margin reflex distance (MRD), eyelid retraction distance, eyelid length, scleral area, and mid-pupil lid distance (MPLD), were automatically calculated using our deep learningbased analysis system. MRD1 and 2 were manually measured. Bland-Altman plots and intraclass correlation coefficients (ICCs) were performed to assess the agreement between automatic and manual measurements of MRDs. The asymmetry of the eyelid contour was analyzed using the temporal: nasal ratio of the MPLD. All eyelid features were compared between TAO eyes and control eyes using the independent samples t-test.Results: A strong agreement between automatic and manual measurement was indicated. Biases of MRDs in TAO eyes and control eyes ranged from −0.01 mm [95% limits of agreement (LoA): −0.64 to 0.63 mm] to 0.09 mm (LoA: −0.46 to 0.63 mm). ICCs ranged from 0.932 to 0.980 (P<0.001). Eyelid features were significantly different in TAO eyes and control eyes, including MRD1 (4.82±1.59 vs.2.99±0.81 mm; P<0.001), MRD2 (5.89±1.16 vs. 5.47±0.73 mm; P=0.009), upper eyelid length (UEL) (27.73±4.49 vs. 25.42±4.35 mm; P=0.002), lower eyelid length (LEL) (31.51±4.59 vs. 26.34±4.72 mm; P<0.001), and total scleral area (SA TOTAL ) (96.14±34.38 vs. 56.91±14.97 mm 2 ; P<0.001). The MPLDs at all angles showed significant differences in the 2 groups of eyes (P=0.008 at temporal 180°; P<0.001 at other angles). The greatest temporal-nasal asymmetry appeared at 75° apart from the midline in TAO eyes.Conclusions: Our proposed system allowed automatic, comprehensive, and objective measurement of eyelid morphology by only using facial images, which has potential application prospects in TAO. Future work with a large sample of patients that contains different TAO subsets is warranted.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

Shao¹,

Huang²,

Gao³

et al. 2023

Quant Imaging Med Surg

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“…A preprocessed pair of images was input into the ocular detection module (O) that was developed in our previous work (Fig 1, the ocular detection module). 14,22,23 In the first part, the eyes and periocular area were located, and in the second part, the located eyelid and corneal limbus were segmented. Then, the segmented masks were output for the next step.…”

Section: Development Of the Automatic Postoperative Appearance Predic...mentioning

confidence: 99%

“…These masks were sent into the analyzing module (A) developed in our previous work (Fig 1, the analyzing module). 14,22,23 First, the eyes were rotated parallel, including locating and connecting the center of the pupil and rotating the image to make the connected line horizontal. Next, the 10-mm diameter round sticker attached to the middle of the forehead was detected and segmented.…”

Section: Development Of the Automatic Postoperative Appearance Predic...mentioning

confidence: 99%

“…The lid contour analysis was applied to automatically evaluate and quantify local prediction performance (Fig 3E), which mainly involved the multiple radial midpupil lid distances (MPLDs), defined as the distance between the corneal light reflex and the upper eyelid margin as the primary clinical measurement. 25 The algorithm was developed on the basis of our former work, 14,22,23 referring to the technique described by Milbratz et al 13 and other previous studies, [26][27][28] and consisted of the following steps. The pupil center was detected and set as the origin, and then 13 radial lines at 15 intervals from 0 to 180 were set.…”

Section: Evaluation Of the Predicted Outcomementioning

confidence: 99%

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A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning

Sun

Huang

Zhang

et al. 2022

Ophthalmology Science

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“…Deep learning with convolutional neural networks (CNNs) has reached ideal performance for ophthalmological image segmentation (12). In our previous studies, we have proposed novel approaches for automated measurement of ocular movements and eyelid morphology in healthy volunteers using CNN-based deep learning methods (13,14). Nevertheless, the modified limbus test which we used previously for measurement of IO was limited to participants with normal eyelid morphology and function (15).…”

Section: Introductionmentioning

confidence: 99%

Automated photographic analysis of inferior oblique overaction based on deep learning

Lou¹,

Huang²,

Sun³

et al. 2023

Quant Imaging Med Surg

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Background: Inferior oblique overaction (IOOA) is a common ocular motility disorder. This study aimed to propose a novel deep learning-based approach to automatically evaluate the amount of IOOA.Methods: This prospective study included 106 eyes of 72 consecutive patients attending the strabismus clinic in a tertiary referral hospital. Patients were eligible for inclusion if they were diagnosed with IOOA. IOOA was clinically graded from +1 to +4. Based on photograph in the adducted position, the height difference between the inferior corneal limbus of both eyes was manually measured using ImageJ and automatically measured by our deep learning-based image analysis system with human supervision.Correlation coefficients, Bland-Altman plots and mean absolute deviation (MAD) were analyzed between two different measurements of evaluating IOOA.Results: There were significant correlations between automated photographic measurements and clinical gradings (Kendall's tau: 0.721; 95% confidence interval: 0.652 to 0.779; P<0.001), between automated and manual photographic measurements [intraclass correlation coefficients (ICCs): 0.975; 95% confidence interval: 0.963 to 0.983; P<0.001], and between two-repeated automated photographic measurements (ICCs: 0.998; 95% confidence interval: 0.997 to 0.999; P<0.001). The biases and MADs were 0.10 [95% limits of agreement (LoA): −0.45 to 0.64] mm and 0.26±0.14 mm between automated and manual photographic measurements, and 0.01 (95% LoA: −0.14 to 0.16) mm and 0.07±0.04 mm between two-repeated automated photographic measurements, respectively. Conclusions:The automated photographic measurements of IOOA using deep learning technique were in excellent agreement with manual photographic measurements and clinical gradings. This new approach allows objective, accurate and repeatable measurement of IOOA and could be easily implemented in clinical practice using only photographs.

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A Novel Automatic Morphologic Analysis of Eyelids Based on Deep Learning Methods

Cited by 9 publications

References 26 publications

Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning

Automated photographic analysis of inferior oblique overaction based on deep learning

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