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Journal of the American Veterinary... Aug 2018
Topics: Animals; Diagnosis, Differential; Dog Diseases; Dogs; Female; Hernia, Inguinal; Hysterectomy; Ovariectomy; Pyometra; Radiography, Abdominal
PubMed: 30020008
DOI: 10.2460/javma.253.3.265 -
Journal of the American Veterinary... Oct 2018
Topics: Amniocentesis; Animals; Cestode Infections; Dog Diseases; Dogs; Male; Mesocestoides; Peritoneal Diseases; Radiography, Abdominal; Ultrasonography, Interventional
PubMed: 30211638
DOI: 10.2460/javma.253.7.857 -
The New England Journal of Medicine Nov 2017
Topics: Abdominal Pain; Aged, 80 and over; Duodenal Diseases; Female; Humans; Intestinal Perforation; Pneumoperitoneum; Radiography, Abdominal; Tomography, X-Ray Computed
PubMed: 29141173
DOI: 10.1056/NEJMicm1613914 -
Medical Image Analysis Jul 2024Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant....
Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for a new kidney segmentation task, unseen in training, using between approximately 40% to 60% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6% and 10.2% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.
Topics: Humans; Algorithms; Tomography, X-Ray Computed; Neural Networks, Computer; Machine Learning; Markov Chains; Supervised Machine Learning; Radiography, Abdominal
PubMed: 38640779
DOI: 10.1016/j.media.2024.103181 -
Jornal de Pediatria 2019The objective of this study was to develop and validate a computational tool to assist radiological decisions on necrotizing enterocolitis.
OBJECTIVE
The objective of this study was to develop and validate a computational tool to assist radiological decisions on necrotizing enterocolitis.
METHODOLOGY
Patients that exhibited clinical signs and radiographic evidence of Bell's stage 2 or higher were included in the study, resulting in 64 exams. The tool was used to classify localized bowel wall thickening and intestinal pneumatosis using full-width at half-maximum measurements and texture analyses based on wavelet energy decomposition. Radiological findings of suspicious bowel wall thickening and intestinal pneumatosis loops were confirmed by both patient surgery and histopathological analysis. Two experienced radiologists selected an involved bowel and a normal bowel in the same radiography. The full-width at half-maximum and wavelet-based texture feature were then calculated and compared using the Mann-Whitney U test. Specificity, sensibility, positive and negative predictive values were calculated.
RESULTS
The full-width at half-maximum results were significantly different between normal and distended loops (median of 10.30 and 15.13, respectively). Horizontal, vertical, and diagonal wavelet energy measurements were evaluated at eight levels of decomposition. Levels 7 and 8 in the horizontal direction presented significant differences. For level 7, median was 0.034 and 0.088 for normal and intestinal pneumatosis groups, respectively, and for level 8 median was 0.19 and 0.34, respectively.
CONCLUSIONS
The developed tool could detect differences in radiographic findings of bowel wall thickening and IP that are difficult to diagnose, demonstrating the its potential in clinical routine. The tool that was developed in the present study may help physicians to investigate suspicious bowel loops, thereby considerably improving diagnosis and clinical decisions.
Topics: Enterocolitis, Necrotizing; Humans; Image Processing, Computer-Assisted; Infant, Newborn; Infant, Newborn, Diseases; Intestines; Radiography, Abdominal; Retrospective Studies; Sensitivity and Specificity; Severity of Illness Index; Software Validation; Statistics, Nonparametric; Wavelet Analysis
PubMed: 31679612
DOI: 10.1016/j.jped.2018.05.017 -
The Turkish Journal of Pediatrics 2022To find the predictor of optimal surgical timing for neonatal necrotizing enterocolitis (NEC) patients by analyzing the risk factors of conservative treatment and...
BACKGROUND
To find the predictor of optimal surgical timing for neonatal necrotizing enterocolitis (NEC) patients by analyzing the risk factors of conservative treatment and surgical therapy.
METHODS
Data were collected from 184 NEC patients (Surgery, n=41; conservative treatment, n=143) between the years 2015 and 2019. Data were analyzed by univariate analysis, and multivariate binary logistic regression analysis.
RESULTS
Univariate analysis showed that statistically significant differences between the surgery and conservative treatment groups. The results of multivariate Logistic regression analysis indicated intestinal wall thickening by B-ultrasound and gestational age were independent factors to predict early surgical indications of NEC (p < 0.05). The true positive rate, false positive rate, true negative rate and false negative rate in the diagnosis of necrotic bowel perforation guided by DAAS (Duke abdominal X-ray score) ≥7 and MD7 (seven clinical metrics of metabolic derangement) ≥3 were 12.8%, 0.0%, 100.0% and 87.2%, respectively.
CONCLUSIONS
In summary, the ultrasound examination in NEC children showing thickening intestinal wall and poor intestinal peristalsis indicated for early operation.
Topics: Child; Enterocolitis, Necrotizing; Gestational Age; Humans; Infant, Newborn; Infant, Newborn, Diseases; Intestinal Perforation; Radiography, Abdominal; Retrospective Studies
PubMed: 36082637
DOI: 10.24953/turkjped.2021.5048 -
The Journal of Small Animal Practice Apr 2017To describe clinical and imaging findings in dogs with confirmed gastrointestinal ulceration, to compare findings in dogs with perforated and non-perforated ulcers and... (Comparative Study)
Comparative Study
OBJECTIVES
To describe clinical and imaging findings in dogs with confirmed gastrointestinal ulceration, to compare findings in dogs with perforated and non-perforated ulcers and to estimate the sensitivities of radiography, ultrasonography and computed tomography (CT) for gastrointestinal ulceration and perforation.
METHODS
Retrospective review of medical records of 82 dogs that had a macroscopic ulcer in the gastric or intestinal mucosa diagnosed directly at endoscopy, surgery or necropsy and had survey radiography, ultrasonography or a CT scan of the abdomen during the same period of hospitalisation.
RESULTS
The most frequent clinical signs were vomiting in 88% dogs, haematemesis in 32%, melaena in 31% and weight loss in 7%. The most frequent imaging findings in dogs with non-perforated ulcers were gastrointestinal mural lesion in 56%, mucosal defect compatible with an ulcer in 44% and peritoneal fluid in 21%. In dogs with perforated ulcers the most frequent imaging findings were peritoneal fluid in 83%, gastrointestinal mural lesion in 48%, peritoneal gas in 31% and mucosal defect compatible with an ulcer in 29%. Sensitivities of radiography, ultrasonography and CT were 30, 65 and 67% in dogs with non-perforated ulcers and 79, 86 and 93% in dogs with perforated ulcers, respectively.
CLINICAL SIGNIFICANCE
In dogs with non-perforated ulcers, survey radiography was usually negative whereas ultrasonography and CT frequently enabled detection of the site of the ulcer; in dogs with perforated ulcers, radiography was frequently positive for peritoneal gas and CT was a sensitive modality for both the ulcer and signs of perforation.
Topics: Animals; Dog Diseases; Dogs; Female; Intestinal Diseases; Male; Radiography, Abdominal; Retrospective Studies; Sensitivity and Specificity; Stomach Ulcer; Tomography, X-Ray Computed; Ulcer; Ultrasonography
PubMed: 28276120
DOI: 10.1111/jsap.12631 -
IEEE Transactions on Medical Imaging Aug 2018Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation...
Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.
Topics: Algorithms; Digestive System; Humans; Kidney; Radiographic Image Interpretation, Computer-Assisted; Radiography, Abdominal; Spleen; Tomography, X-Ray Computed
PubMed: 29994628
DOI: 10.1109/TMI.2018.2806309 -
Journal of Medical Radiation Sciences Mar 2022The incidence of obesity has been steadily rising over the last few decades and is having a significant impact upon the health system. In radiography, a particular...
INTRODUCTION
The incidence of obesity has been steadily rising over the last few decades and is having a significant impact upon the health system. In radiography, a particular challenge of imaging obese patients is implementing the as low as reasonably achievable (ALARA) principle when determining radiation dose, and technical and patient-care adaptations. This study aimed to better understand the decision-making strategies of experienced radiographers in determining imaging and exposure factor selection in the context of imaging obese patients.
METHODS
This study employed a 'think-aloud,' methodology, and eight experienced diagnostic radiographers working in clinical education were recruited to perform routine AP abdominal X-ray projections on an anthropomorphic phantom. They were asked to simultaneously verbalise emerging thoughts as they considered positioning, exposure selection and image evaluation. This process was repeated with three different phantom sizes, each representing an increased BMI from 'healthy,' to, 'morbidly obese.' Audio recordings were transcribed and interpreted via Bowman's (1997) theory of radiographic judgement and decision-making.
RESULTS
Analysis of interview transcripts identified 12 key concepts considered by experienced radiographers. Differences in radiographic concepts were considered when imaging phantoms of different sizes was demonstrated. A shift from segmental (e.g. positioning) to more environmental factors (e.g. patient comfort) and an increase in the number of verbal considerations with increasing phantom size were identified. The shift in focus of decision-making stages identified the greater need to consider contextual factors such as patient comfort and repeatability when imaging obese patients.
CONCLUSION
Experienced radiographers find imaging obese patients challenging and alter their perception of image quality to accommodate for patient presentation. The findings will help inform future research, practice guidelines and learning resources to provide optimal imaging and care for obese patients, especially for student education.
Topics: Humans; Obesity, Morbid; Phantoms, Imaging; Radiation Dosage; Radiography, Abdominal
PubMed: 34496140
DOI: 10.1002/jmrs.543 -
BMJ Case Reports Nov 2020A 52-year-old woman was diagnosed with unresectable gallbladder neuroendocrine carcinoma (GB-NEC) exhibiting lymph node and peritoneal metastases, and received eight...
A 52-year-old woman was diagnosed with unresectable gallbladder neuroendocrine carcinoma (GB-NEC) exhibiting lymph node and peritoneal metastases, and received eight courses of chemotherapy with irinotecan plus cisplatin. Radiological examinations revealed significant regression of the GB tumour and disappearance of metastatic lesions, so the patient underwent laparoscopic cholecystectomy. However, the patient presented with multiple haemorrhagic brain metastases (BMs) and died 13 months after the initial diagnosis despite neurosurgical interventions. Pathological examination of the resected gallbladder demonstrated an extensive fibrous scar along with tubular adenocarcinoma components, which may indicate that the chemotherapy eliminated a pre-existing neuroendocrine carcinoma (NEC) component. Furthermore, pathological analysis confirmed that the BMs comprised NEC. In patients with advanced GB-NEC, conversion surgery may be a reasonable option if a first-line chemotherapy leads to downstaging of the tumour. Second-line drug therapy and systemic screening might also be considered in cases with BMs.
Topics: Antineoplastic Agents; Brain; Brain Neoplasms; Carcinoma, Neuroendocrine; Cholecystectomy, Laparoscopic; Combined Modality Therapy; Fatal Outcome; Female; Gallbladder; Gallbladder Neoplasms; Humans; Middle Aged; Radiography, Abdominal; Tomography, X-Ray Computed
PubMed: 33257386
DOI: 10.1136/bcr-2020-238114