53 research outputs found
Diagnostic Imaging in Snakes and Lizards
The increasing popularity of snakes and lizards as pets has led to an increasing demand of specialised veterinary duties in these animals. Diagnostic imaging is often a fundamental step of the clinical investigation. The interpretation of diagnostic images is complex and requires a broad knowledge of anatomy, physiology and pathology of the species object of the clinical investigation. Moreover, in order to achieve a correct diagnosis, the comparison between normal and abnormal diagnostic images, for all the diagnostic imaging modalities, is mandatory. In this PhD thesis the diagnostic imaging features of some snake and lizard species are described. The aim of all the works presented is to provide some normal atlases matching the normal gross and cross-sectional anatomy with the normal radiographic, ultrasonographic, CT features of some of the most popular pet lizard and snake specie.
In Chapter I a review of literature regarding snakes and lizards is presented. The aim of this chapter is to review the most commonly used diagnostic imaging modalities as well as to make an updated collection of the available international references describing the normal and pathological imaging features in snakes and lizards. Most of papers describing radiography, ultrasonography, computed tomography, magnetic resonance imaging and other imaging modalities have been collected in order to overcome the lack of a unique reference regarding diagnostic imaging in snakes and lizards.
The scientific aims and the outline of this thesis are presented in Chapter II. The general aim of this PhD thesis is to provide some useful anatomical and diagnostic imaging references in snakes and lizards. The first part of this work (Chapters III and IV) is focused on the description of the normal radiographic and computed tomographic features of the head of some snakes and lizards species. The second part (Chapters V to VII) is focused on the diagnostic imaging of the coelomic cavity; the description of the normal contrast enhanced computed tomographic features of the coelomic cavity of some lizards, the normal ultrasonographic features of the coelomic cavity of some snake species and the normal upper gastro-intestinal examination in the ball python are presented.
In Chapter III the normal stratigraphic and cross sectional anatomy is matched with the normal radiographic and computed tomographic features of the head of the Boa constrictor. 4 boa constrictor’s cadavers head where used in this study. Radiographs of the head were taken in LL and DV projections using a high detail screen-film combination. CT scans scans of the head where performed in a CC and a LL direction with a slice thickness of 1,5mm and displayed in a bone window. 2 heads where dissected following a stratigraphic approach and 2 heads frozen for 24h (-20°C) and then sectioned into 3mm slices respecting the imaging protocol. All anatomical structures have been identified and labelled with the aid of available literature in the anatomical images and then matched on the corresponding radiographic and computed tomographic images. Radiographic and CT images provided a high detail for the visualisation of bony structures; soft tissues were not easily identified on radiographic and CT images.
In Chapter IV the normal radiographic and contrast enhanced computed tomographic features of the head of the green iguana, common tegu and bearded dragon are described. The study included 4 cadavers for each considered species and 6 adult green iguanas, 4 tegus, 3 bearded dragons. Prior to the beginning of the radiographic and computed tomographic studies 2 cadavers were dissected following a stratigraphic approach and 2 cadavers were cross-sectioned for each species. Anatomical studies were performed following the same approach described in Chapter III. Both the radiographic and the computed tomographic studies were performed only in live animals. Radiographic studies included a LL and a DV projection. Pre- and post- contrast computed tomographic studies of the head were performed in a CC direction. CT images were displayed in both bone and soft tissue windows. Individual anatomical structures were first recognised and labelled on the anatomic images and then matched on radiographs and CT images. Radiographic studies provided a good detail both of the soft tissues (especially in the green iguana) and of the bony structures. CT images provided an excellent detail of the bony structures in all the considered species. The soft tissues were clearly outlined only in the green iguana. In the common tegu and the bearded dragon only the eyes were clearly outlined from the remaining soft tissues.
In Chapter V the normal contrast enhanced computed tomographic features of the coelomic cavity of the green iguana, black and white tegu and the bearded dragon are described. 4 cadavers and 4 live animals for each considered species were object of this study. The cadavers were frozen for 24 hours and then cross sectioned at 5mm intervals. The slices have been cleaned with water and photographed on both sides. In order to reduce the duration of the procedure only contrast enhanced CT scans have been performed. The CT scans have been performed in a CC direction. The CT scans have been displayed in a soft tissue and, when appropriate, in a lung window. Individual organs have been recognised and labelled on the anatomical images and then matched on the corresponding CT images. Most of the coelomic organs have been identified in all the considered species. Results provide an atlas of the normal cross sectional and CT features of the coelomic cavity of lizards.
In Chapter VI the normal ultrasonographic features of the coelomic cavity of the Boa constrictor, Python molurus, Python curtus and Python regius are described. Moreover, normal reference ultrasonographic measurements of the scent glans, the colonic, gastric and pyloric wall thickness are reported. 46 live snakes (16 Python regius, 10 Python molurus, 12 Python curtus and 8 Boa constrictor) and 23 cadavers (6 Python regius, 4 Python molurus, 10 Python curtus, 3 Boa constrictor) where object of this study. Anatomical studies where performed prior to the beginning of the ultrasonographic studies in order to characterise the normal anatomical features of the above mentioned species. In previous ultrasonographic studies of the coelomic cavity of the Boa constrictor studies a ventral approach on sedated animals was proposed. We have decided to use a lateral approach on unsedated animals. Although, especially in larger animals, the shadowing effect produced by the ribs was evident in some images, most of the coelomic organs (scent glands, hemipenes, cloaca, ureters, colon, small intestine, pylorus, stomach, pancreas, liver, gallbladder and oesophagus) have been recognised. The rate of ultrasonographic recognition of individual organs is reported. Results provide a description of the normal ultrasonographic features of coelomic cavity of boid snakes along with a series of tables matching the gross and cross sectional anatomy with corresponding normal ultrasonographic images.
In Chapter VII the technique and the normal features of upper gastro-intestinal examination in ball pythons are described. 10 ball python's cadavers have been dissected and cross sectioned prior to the the beginning of the study in order to characterise the normal features of the intestine in this species.18 healthy ball pythons where object of this study. All animals where not fed for at least 7 days before the beginning of the study. The animals have been divided into three groups (A, B, C). Contrast medium (barium sulphate) at the dose of 25 ml/kg has been administered through an esophageal probe at an increasing concentration (25%, 35% and 45 wt/vol) to three groups. An initial animal (Group A , 25% wt/vol) was used to verify the feasibility and establish a time course for the procedure. Imaging quality was evaluated by 3 investigators who assigned a grading score on the basis of predetermined criteria. Results of present study revealed that the 35% wt/vol concentration of contrast medium provided the best imaging quality. Moreover, three pattern of distribution of the contrast medium in the small intestine, independent from the concentration, have been described
Relationship of diagnostic accuracy of renal cortical echogenicity with renal histopathology in dogs and cats, a quantitative study
Renal cortical echogenicity is routinely evaluated during ultrasonographic investigation of the kidneys. Both in dog and cat previous ex-vivo studies have revealed a poor correlation between renal echogenicity and corresponding lesions. The aim of this study was to establish the in-vivo relationship between renal cortical echogenicity and renal histopathology
Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images
Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs
The purpose of this study was to develop a computer-aided detection (CAD) device based on convolutional neural networks (CNNs) to detect cardiomegaly from plain radiographs in dogs. Right lateral chest radiographs (n = 1465) were retrospectively selected from archives. The radiographs were classified as having a normal cardiac silhouette (No-vertebral heart scale [VHS]-Cardiomegaly) or an enlarged cardiac silhouette (VHS-Cardiomegaly) based on the breed-specific VHS. The database was divided into a training set (1153 images) and a test set (315 images). The diagnostic accuracy of four different CNN models in the detection of cardiomegaly was calculated using the test set. All tested models had an area under the curve >0.9, demonstrating high diagnostic accuracy. There was a statistically significant difference between Model C and the remainder models (Model A vs. Model C, P = 0.0298; Model B vs. Model C, P = 0.003; Model C vs. Model D, P = 0.0018), but there were no significant differences between other combinations of models (Model A vs. Model B, P = 0.395; Model A vs. Model D, P = 0.128; Model B vs. Model D, P = 0.373). Convolutional neural networks could therefore assist veterinarians in detecting cardiomegaly in dogs from plain radiographs
correlation between renal histopathology and renal ultrasound in dogs
Abstract Fifty-three privately owned dogs were included in the study. Ultrasonography of the kidneys was performed ante mortem. All the dogs died or were euthanized for reasons unrelated to this study. Histopathology of both kidneys was performed, and a degeneration and an inflammation score ranging from zero to two was assigned by consensus between two pathologists. A numerical score based on a three level semi-quantitative scale (0, 0.5, 1) was assigned by consensus between two of the authors to the following ultrasonographic abnormalities: cortico-medullary definition, echogenicity of the renal cortex, echogenicity of the medulla, renal shape, cysts, scars, mineralizations, subcapsular perirenal fluid accumulation, pyelectasia. The scores deriving from the consensus were summed to create a summary index called renal ultrasound score (RUS). Statistically significant differences in cortico-medullary definition, echogenicity of the renal cortex, echogenicity of the medulla, renal shape, scars and pyelectasia were evident between the degeneration score groups. There were significantly different distributions of cortico-medullary definition, renal shape and scars between the inflammatory score groups. There were statistically significant differences in the RUS between the degenerative score groups (F = 24.154, p-valu
Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: a preliminary study
Background: Grading of meningiomas is important in the choice of the most effective treatment for each patient.
Purpose: To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of
the histopathological grading of meningiomas from MR images.
Study Type: Retrospective.
Population: In all, 117 meningioma-affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II,
and 6 WHO Grade III.
Field Strength/Sequence: 1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1W), apparent diffusion coefficient (ADC) maps
(b values of 0, 500, and 1000 s/mm2).
Assessment: WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy
of the pretrained Inception-V3 and AlexNet DCNNs was tested on ADC maps and PCT1W images separately. Receiver
operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance.
Statistical Test: Leave-one-out cross-validation.
Results: The application of the Inception-V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an
AUC of 0.94 (95% confidence interval [CI], 0.88\u20130.98). Remarkably, only 1/38 WHO Grade II\u2013III and 7/79 WHO Grade I
lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with
an AUC of 0.68 (95% CI, 0.59\u20130.76) and a high misclassification rate on both WHO Grade I and WHO Grade II\u2013III cases.
The discriminating accuracy of both DCNNs on postcontrast T1W images was low, with Inception-V3 displaying an AUC of
0.68 (95% CI, 0.59\u20130.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45\u20130.64).
Data Conclusion: DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC
maps but not from PCT1W images.
Level of evidence: 2
Technical Efficacy: Stage
Normal computed tomographic features and reference values for the coelomic cavity in pet parrots
BACKGROUND: The increasing popularity gained by pet birds over recent decades has highlighted the role of avian medicine and surgery in the global veterinary scenario; such a need for speciality avian medical practice reflects the rising expectation for high-standard diagnostic imaging procedures. The aim of this study is to provide an atlas of matched anatomical cross-sections and contrast-enhanced CT images of the coelomic cavity in three highly diffused psittacine species. RESULTS: Contrast-enhanced computed tomographic studies of the coelomic cavity were performed in 5 blue-and-gold macaws, 4 African grey parrots and 6 monk parakeets by means of a 4-multidetector-row CT scanner. Both pre- and post-contrast scans were acquired. Anatomical reference cross-sections were obtained from 5 blue-and-gold macaw, 7 African grey parrot, and 9 monk parakeet cadavers. The specimens were stored in a −20 °C freezer until completely frozen and then sliced at 5-mm intervals by means of a band saw. All the slices were photographed on both sides. Individual anatomical structures were identified by means of the available literature. Pre- and post-contrast attenuation reference values for the main coelomic organs are reported in Hounsfield units (HU). CONCLUSIONS: The results provide an atlas of matched anatomical cross-sections and contrast-enhanced CT images of the coelomic cavity in three highly diffused psittacine species
Correlation of renal histopathology with renal echogenicity in dogs and cats: An ex-vivo quantitative study
BACKGROUND: Increased cortical or cortical and medullary echogenicity is one of the most common signs of chronic or acute kidney disease in dogs and cats. Subjective evaluation of the echogenicity is reported to be unreliable. Patient and technical-related factors affect in-vivo quantitative evaluation of the echogenicity of parenchymal organs. The aim of the present study is to investigate the relationship between histopathology and ex-vivo renal cortical echogenicity in dogs and cats devoid of any patient and technical-related biases. RESULTS: Kidney samples were collected from 68 dog and 32 cat cadavers donated by the owners to the Veterinary Teaching Hospital of the University of Padua and standardized ultrasonographic images of each sample were collected. The echogenicity of the renal cortex was quantitatively assessed by means of mean gray value (MGV), and then histopathological analysis was performed. Statistical analysis to evaluate the influence of histological lesions on MGV was performed. The differentiation efficiency of MGV to detect pathological changes in the kidneys was calculated for dogs and cats. Statistical analysis revealed that only glomerulosclerosis was an independent determinant of echogenicity in dogs whereas interstitial nephritis, interstitial necrosis and fibrosis were independent determinants of echogenicity in cats. The global influence of histological lesions on renal echogenicity was higher in cats (23%) than in dogs (12%). CONCLUSIONS: Different histopathological lesions influence the echogenicity of the kidneys in dogs and cats. Moreover, MGV is a poor test for distinguishing between normal and pathological kidneys in the dog with a sensitivity of 58.3% and specificity of 59.8%. Instead, it seems to perform globally better in the cat, resulting in a fair test, with a sensitivity of 80.6% and a specificity of 56%
Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs
An algorithm based on artificial intelligence (AI) was developed and tested to classify different stages of myxomatous mitral valve disease (MMVD) from canine thoracic radiographs. The radiographs were selected from the medical databases of two different institutions, considering dogs over 6 years of age that had undergone chest X-ray and echocardiographic examination. Only radiographs clearly showing the cardiac silhouette were considered. The convolutional neural network (CNN) was trained on both the right and left lateral and/or ventro-dorsal or dorso-ventral views. Each dog was classified according to the American College of Veterinary Internal Medicine (ACVIM) guidelines as stage B1, B2 or C + D. ResNet18 CNN was used as a classification network, and the results were evaluated using confusion matrices, receiver operating characteristic curves, and t-SNE and UMAP projections. The area under the curve (AUC) showed good heart-CNN performance in determining the MMVD stage from the lateral views with an AUC of 0.87, 0.77, and 0.88 for stages B1, B2, and C + D, respectively. The high accuracy of the algorithm in predicting the MMVD stage suggests that it could stand as a useful support tool in the interpretation of canine thoracic radiographs
Anatomia tomografica normale del coniglio: studio comparato tra l' anatomia cadaverica e la tomografia computerizzata a Raggi-x.
Studio comparato tra la normale anatomia per sezioni del coniglio e le corrispondenti immagini in tomografia computerizzata a raggi X. Le sezioni partono dalla punta del naso e terminano alla parte più caudale dell'ano
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