Semantic Segmentation of Dog’s Femur and Acetabulum Bones with Deep Transfer Learning in X-Ray Images

Silva, D. E. Moreira da; Filipe, Vítor; Franco-Gonçalo, Pedro; Colaço, Bruno; Alves‐Pimenta, Sofia; Ginja, Mário; Gonçalves, Lio

doi:10.1007/978-3-030-96308-8_43

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…The acetabulum and the proximal femur were delimitated using the image polygonal annotation tool (LabelMe version 4.5.13 accessed between 1 January and 31 March 2022) [ 16 ]. The acetabular area (AA) and the acetabular area occupied by the femoral head (AAOFH) were measured and the HCI was calculated by dividing AAOFH by AA ( Figure 2 ).…”

Section: Methodsmentioning

confidence: 99%

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Acetabular Coverage Area Occupied by the Femoral Head as an Indicator of Hip Congruency

Franco-Gonçalo

Silva

Leite³

et al. 2022

Animals

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Accurate radiographic screening evaluation is essential in the genetic control of canine HD, however, the qualitative assessment of hip congruency introduces some subjectivity, leading to excessive variability in scoring. The main objective of this work was to validate a method-Hip Congruency Index (HCI)-capable of objectively measuring the relationship between the acetabulum and the femoral head and associating it with the level of congruency proposed by the Fédération Cynologique Internationale (FCI), with the aim of incorporating it into a computer vision model that classifies HD autonomously. A total of 200 dogs (400 hips) were randomly selected for the study. All radiographs were scored in five categories by an experienced examiner according to FCI criteria. Two examiners performed HCI measurements on 25 hip radiographs to study intra- and inter-examiner reliability and agreement. Additionally, each examiner measured HCI on their half of the study sample (100 dogs), and the results were compared between FCI categories. The paired t-test and the intraclass correlation coefficient (ICC) showed no evidence of a systematic bias, and there was excellent reliability between the measurements of the two examiners and examiners’ sessions. Hips that were assigned an FCI grade of A (n = 120), B (n = 157), C (n = 68), D (n = 38) and E (n = 17) had a mean HCI of 0.739 ± 0.044, 0.666 ± 0.052, 0.605 ± 0.055, 0.494 ± 0.070 and 0.374 ± 0.122, respectively (ANOVA, p < 0.01). Therefore, these results show that HCI is a parameter capable of estimating hip congruency and has the potential to enrich conventional HD scoring criteria if incorporated into an artificial intelligence algorithm competent in diagnosing HD.

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Section: Methodsmentioning

confidence: 99%

“…In some of these technological solutions, images with specific annotations are used as ground-truth data to train computer vision models. This allows them to successfully identify appropriate anatomical landmarks and subsequently give correct classification in novel images presented to the model after training [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Acetabular Coverage Area Occupied by the Femoral Head as an Indicator of Hip Congruency

Franco-Gonçalo

Silva

Leite³

et al. 2022

Animals

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“…Moreira da Silva et al (2022) used a U-net for femur and acetabulum segmentation and active learning to maximize the model’s performance with the least amount of data. This led to the creation of a high-performing model which required 18.98% less annotated data [ 25 , 35 ].…”

Section: Veterinary Imagingmentioning

confidence: 99%

“…Precision × Recall Precision + Recall Dice score is generally used in segmentation. If the region of interest annotated by the expert and the one predicted by the model overlap completely, the score is one; if they do not overlap at all, the score is 0 [1,35].…”

Section: Sensitivity =mentioning

confidence: 99%

Artificial Intelligence in Veterinary Imaging: An Overview

Pereira

Franco-Gonçalo

Leite³

et al. 2023

Veterinary Sciences

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Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.

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“…The NA was calculated between the line joining the centers of the femoral heads and another line connecting the center of the femoral head to the ipsilateral craniodorsal effective acetabular rim (1,18). The HCI was calculated by image segmentation delineating the acetabulum and the femur and dividing the acetabular area occupied by the femoral head by the acetabular area (7,19).…”

Section: Radiographic Measurementsmentioning

confidence: 99%

Femoral parallelism: evaluation and impact of variation on canine hip dysplasia assessment

et al. 2023

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Adequate radiographic positioning on the X-ray table is paramount for canine hip dysplasia (HD) screening. The aims of this study were to evaluate femoral parallelism on normal ventrodorsal hip extended (VDHE) view and the effect of femoral angulation (FA) on Norberg Angle (NA) and Hip Congruency Index (HCI). The femoral parallelism was evaluated comparing the alignment of the long femoral axis with the long body axis in normal VDHE views and the effect of FA on NA and HCI on repeated VDHE views with different levels of FA. The femoral long axis in normal VDHE views showed a ranged of FA from −4.85° to 5.85°, mean ± standard deviation (SD) of −0.06 ± 2.41°, 95% CI [−4.88, 4.76°]. In the paired views, the mean ± SD femur adduction of 3.69 ± 1.96° led to a statistically significant decrease NA, and HCI, and femur abduction of 2.89 ± 2.12 led to a statistically significant increase in NA and HCI (p < 0.05). The FA differences were also significantly correlated with both NA differences (r = 0.83) and HCI differences (r = 0.44) (p < 0.001). This work describes a methodology that allows evaluation of femoral parallelism in VDHE views and the results suggest that femur abduction yielded more desirable NA and HCI values and adduction impaired NA and HCI values. The positive linear association of FA with NA and HCI allows the use of regression equations to create corrections, to reduce the influence of poor femoral parallelism in the HD scoring.

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Semantic Segmentation of Dog’s Femur and Acetabulum Bones with Deep Transfer Learning in X-Ray Images

Cited by 4 publications

References 20 publications

Acetabular Coverage Area Occupied by the Femoral Head as an Indicator of Hip Congruency

Acetabular Coverage Area Occupied by the Femoral Head as an Indicator of Hip Congruency

Artificial Intelligence in Veterinary Imaging: An Overview

Femoral parallelism: evaluation and impact of variation on canine hip dysplasia assessment

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