Deep transfer learning can be used for the detection of hip joints in pelvis radiographs and the classification of their hip dysplasia status

McEvoy, Fintan J.; Proschowsky, Helle Friis; Müller, Anna V.; Moorman, Lilah; Bender-Koch, Johan; Svalastoga, E.; Frellsen, Jes; Nielsen, Dorte H.

doi:10.1111/vru.12968

Cited by 21 publications

(18 citation statements)

References 20 publications

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“…In the last few years, an increasing number of research papers exploring the possible applications of machine learning in veterinary radiology have been published ( 9 – 15 ). Research in this field has mostly been focused on the automatic classification of radiographic images ( 14 , 16 , 17 ), the distinction between benign and malignant brain lesions on MRI ( 10 , 18 ), and the classification of liver focal lesion types on CT images ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features

et al. 2022

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The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features

et al. 2022

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“…In veterinary medicine, studies have proved that CNN models are efficacious for classification issues with canine medical images. CNNs are able to classify superficial or deep corneal ulcers in photographs 9 , to detect diffuse degenerative hepatic diseases in ultrasound images 10 , to distinguish between meningiomas and gliomas in MR-images 11 , to detect cardiomegaly in TR images 12 and to detect hip joints in pelvis radiographs and to classify their hip dysplasia status 13 . In TR images in particular, a CNN-based approach was more efficacious than veterinarians to evaluate ventricular and left atrial enlargement, cardiomegaly and bronchial RPP in feline and canine radiographs 5 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats

Dumortier

Guépin

Delignette-Muller

et al. 2022

Sci Rep

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Thoracic radiograph (TR) is a complementary exam widely used in small animal medicine which requires a sharp analysis to take full advantage of Radiographic Pulmonary Pattern (RPP). Although promising advances have been made in deep learning for veterinary imaging, the development of a Convolutional Neural Networks (CNN) to detect specifically RPP from feline TR images has not been investigated. Here, a CNN based on ResNet50V2 and pre-trained on ImageNet is first fine-tuned on human Chest X-rays and then fine-tuned again on 500 annotated TR images from the veterinary campus of VetAgro Sup (Lyon, France). The impact of manual segmentation of TR’s intrathoracic area and enhancing contrast method on the CNN’s performances has been compared. To improve classification performances, 200 networks were trained on random shuffles of training set and validation set. A voting approach over these 200 networks trained on segmented TR images produced the best classification performances and achieved mean Accuracy, F1-Score, Specificity, Positive Predictive Value and Sensitivity of 82%, 85%, 75%, 81% and 88% respectively on the test set. Finally, the classification schemes were discussed in the light of an ensemble method of class activation maps and confirmed that the proposed approach is helpful for veterinarians.

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“…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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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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Deep transfer learning can be used for the detection of hip joints in pelvis radiographs and the classification of their hip dysplasia status

Cited by 21 publications

References 20 publications

A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features

A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features

Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats

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

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