Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis

Sun, Lir-Fang; Kong, Qi; Huang, Yan; Yang, Jiushan; Wang, Shaoshan; Zou, Ruiqi; Yin, Yilong; Peng, Jingliang

doi:10.1155/2020/1782531

Cited by 10 publications

(9 citation statements)

References 36 publications

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“…Sun et al. [48] used active contour model for the femur and patella segmentation to diagnose patellar dislocation. The CT images were initially subjected to contrast enhancement.…”

Section: Knee Image Segmentation Approachesmentioning

confidence: 99%

Review of automated segmentation approaches for knee images

Ridhma

Kaur

Sofat

et al. 2020

IET Image Processing

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Knee disorders are common among the human population. Knee osteoarthritis (OA) is the most widespread knee joint disorder, which may require surgical treatment. The detection and diagnosis of knee joint disorders from medical images demand enormous human effort and time. The development of a computer‐aided diagnosis (CAD) system can notably minimise the burden of medical experts and remove the intra‐observer and inter‐observer variations. To achieve the goal, the highly challenging research problem of knee image segmentation has been frequently paid attention in past years, which can be efficiently applied in the development of the CAD system. Knee image segmentation is a challenging task owing to the image contrasts, intensity variations, shape irregularities, and the presence of thin cartilage structures. Therefore, this paper presents a literature review of automated segmentation approaches mainly focused on the segmentation of knee cartilage and bone, with respect to the underlying technical aspects, datasets used, and the performance reported. The paper also presents the growth from classical segmentation approaches towards the deep learning approaches in the knee image segmentation. Owing to the varying quality and complexity of different knee image datasets, this paper abstains from doing a rigorous comparative evaluation of image segmentation approaches.

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“…Sun et al. [48] used active contour model for the femur and patella segmentation to diagnose patellar dislocation. The CT images were initially subjected to contrast enhancement.…”

Section: Knee Image Segmentation Approachesmentioning

confidence: 99%

Review of automated segmentation approaches for knee images

Ridhma

Kaur

Sofat

et al. 2020

IET Image Processing

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“…However, HTO can also affect the postoperative bone structure of the patellofemoral joint, resulting in patella alta or patella baja compared to the preoperative situation. Therefore, measurement methods [ 15 ] such as CDI [ 16 ] and BPI [ 16 ] are often used to test whether the height of the patella changes.…”

Section: Introductionmentioning

confidence: 99%

Patellar Height after High Tibial Osteotomy of the Distal Tibial Tuberosity: A Retrospective Study of Age Stratification

Ding

Tan

Tian

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. To explore the effect of age stratification on patellar height after single-plane high tibial osteotomy of the distal tibial tuberosity (DTT-HTO). Methods. A retrospective analysis was performed on 110 knee joints undergoing DTT-HTO. Patients were divided into three groups according to age: under 60 years old, 28 cases; 60 to 70 years old, 61 cases; and over 70 years old, 21 cases. All patients were followed up for no less than 12 months, and at each follow-up, short-leg radiographs and whole-leg radiographs were taken. The values of the Caton-Deschamps index (CDI) and Blackburne-Peel index (BPI) of single-short-leg radiographs and the femoral-tibial angle (FTA) and weight-bearing line ratio (WBLR) of whole-leg radiographs were measured before and at the last follow-up. The Lysholm score before and at the last follow-up and the visual analogue scale (VAS) score before and 3 days after surgery and at the last follow-up were calculated. The frequency of classification of the normal-height patella, patella alta, and patella baja before and after surgery was recorded. Results. There were no significant differences in CDI and BPI preoperatively or postoperatively among the three groups ( P > 0.0 5), and there were no statistically significant differences in FTA and WBLR. There were no significant differences in CDI, BPI, FTA, or WBLR between the three groups before and after the operation ( P > 0.05 ). The Lysholm score increased from 48.84 ± 10.10 before surgery to 91.96 ± 3.082 after surgery ( P < 0.05 ); the VAS score decreased from 8.23 ± 0.99 before surgery to 1.93 ± 0.953 at 3 days after surgery and 1.07 ± 0.53 at the last follow-up ( P < 0.01 ). No significant difference was observed in the incidence of each patellar height classification between the three groups preoperatively and postoperatively. Conclusion. Patellar height is not influenced by DTT-HTO. The age of patients is not a limiting factor for the selection of this surgical procedure. Without affecting the height of the patella, DTT-HTO can effectively reduce pain in the knee joint, restore the function of the knee joint, and delay the progression of patellar arthritis.

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“…Secondly, the key points were selected from the segmented regions based on boundary tracing and inflection point detection, and then the PTA and PLS values are measured. 8 The major drawback of this work was the manual identification of key points from segmented femur and patella boundaries, which may result in inter-and intra-observer variations. Therefore, a more robust and fully automated technique will significantly help the experts to diagnose patellar dislocation.…”

Section: Introductionmentioning

confidence: 99%

“…In the first step, the regions of interest (ROI), that is, femur and patella were segmented using the active contour model following contrast enhancement. Secondly, the key points were selected from the segmented regions based on boundary tracing and inflection point detection, and then the PTA and PLS values are measured 8 . The major drawback of this work was the manual identification of key points from segmented femur and patella boundaries, which may result in inter‐ and intra‐observer variations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated measurement of sulcus angle on axial knee magnetic resonance images

Ridhma

Kaur

Sofat

et al. 2021

Int J Imaging Syst Tech

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Patellar dislocation is a disorder in the human knee where patella slips out of its expected position. The orthopedic experts need to manually measure the parameters indicating patellar instability from knee scans, which is a laborious and time-consuming task. An automated method for measuring these parameters can resolve the issue of inter-observer and intra-observer variations and significantly lessen the burden of the experts. Therefore, in this work, a two-

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Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis

Cited by 10 publications

References 36 publications

Review of automated segmentation approaches for knee images

Review of automated segmentation approaches for knee images

Patellar Height after High Tibial Osteotomy of the Distal Tibial Tuberosity: A Retrospective Study of Age Stratification

Automated measurement of sulcus angle on axial knee magnetic resonance images

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