Articular Cartilage Defect Detection Based on Image Segmentation with Colour Mapping

Kubíček, Jan; Penhaker, Marek; Bryjova, Iveta; Kodaj, Michal

doi:10.1007/978-3-319-11289-3_22

Cited by 13 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, this method is inapplicable for patients with OA due to intensity tuning and boundary discontinuities in defected cartilage areas. However, conventional algorithms have been used in a number of studies for automated segmentation of symptomatic knee OA with defected cartilage surfaces [89,90,91].…”

Section: Edge-basedmentioning

confidence: 99%

“…Another successful approach to overcome the issue of uncertain intensity boundaries in segmenting the pathological knee MRIs was proposed by Kubicek et al [90,91]. In their multiregional fuzzy thresholding approach, a membership function is defined for pixels whose intensity values are within a region (i.e., within the same tissue) [90]. Fuzzy (soft) thresholding method had been previously proposed in [93] in which fuzzy c-means clustering (FCM) [94] is used to obtain the centroids of the clusters.…”

Section: Edge-basedmentioning

confidence: 99%

See 1 more Smart Citation

A review on segmentation of knee articular cartilage: from conventional methods towards deep learning

Ebrahimkhani

Jaward

Cicuttini

et al. 2020

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. It has been traditionally applied in quantifying longitudinal knee OA progression pattern to detect and assess the articular cartilage thickness and volume. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that state-of-the-art techniques based on DL outperform the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches has achieved the best results (mean Dice similarity coefficient (DSC) between 85.8% and 90%).

show abstract

Section: Edge-basedmentioning

confidence: 99%