scholarly journals Novel Biased Normalized Cuts Approach for the Automatic Segmentation of the Conjunctiva

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 997 ◽  
Author(s):  
Giovanni Dimauro ◽  
Lorenzo Simone
Keyword(s):  
Automatic Segmentation ◽  
Region Of Interest ◽  
Hemoglobin Concentration ◽  
Diagnostic Procedures ◽  
Semantic Segmentation ◽  
Objective Evaluation ◽  
Daily Lives ◽  
Normalized Cuts ◽  
Segmentation Approach ◽  
Physical Examinations

Anemia is a common public health disease diffused worldwide. In many cases it affects the daily lives of patients needing medical assistance and continuous monitoring. Medical literature states empirical evidence of a correlation between conjunctival pallor on physical examinations and its association with anemia diagnosis. Although humans exhibit a natural expertise in pattern recognition and associative skills based on hue properties, the variance of estimates is high, requiring blood sampling even for monitoring. To design automatic systems for the objective evaluation of pallor utilizing digital images of the conjunctiva, it is necessary to obtain reliable automatic segmentation of the eyelid conjunctiva. In this study, we propose a graph partitioning segmentation approach. The semantic segmentation procedure of a diagnostically meaningful region of interest has been proposed for exploiting normalized cuts for perceptual grouping, thereby introducing a bias towards spectrophotometry features of hemoglobin. The reliability of the identification of the region of interest is demonstrated both with standard metrics and by measuring the correlation between the color of the ROI and the hemoglobin level based on 94 samples distributed in relation to age, sex and hemoglobin concentration. The region of interest automatically segmented is suitable for diagnostic procedures based on quantitative hemoglobin estimation of exposed tissues of the conjunctiva.

Automated Pancreas Segmentation And Volumetry Using Deep Neural Network On Computed Tomography

2021 ◽  
Author(s):  
Sang-Heon Lim ◽  
Young Jae Kim ◽  
Yeon-Ho Park ◽  
Doojin Kim ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
External Validation ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Similarity Coefficients ◽  
Internal Validation ◽  
Pancreas Segmentation

Abstract Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1,006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the Cancer Imaging Archive (TCIA) pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

2021 ◽  
pp. 016173462110425
Author(s):  
Jianing Xi ◽  
Jiangang Chen ◽  
Zhao Wang ◽  
Dean Ta ◽  
Bing Lu ◽  
...  
Keyword(s):  
Congenital Anomaly ◽  
Large Scale ◽  
Automatic Segmentation ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Attention Mechanism ◽  
Scale Model ◽  
Ultrasound Images ◽  
Multi Scale ◽  
Task Irrelevant

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in fetal hearts and lungs. To reduce the intensive cost during manual recognition of organ regions, many automatic segmentation methods have been proposed. However, the existing methods still encounter multi-scale problem at a larger range of receptive fields of organs in images, resolution problem of segmentation mask, and interference problem of task-irrelevant features, obscuring the attainment of accurate segmentations. To achieve semantic segmentation with functions of (1) extracting multi-scale features from images, (2) compensating information of high resolution, and (3) eliminating the task-irrelevant features, we propose a multi-scale model with skip connection framework and attention mechanism integrated. The multi-scale feature extraction modules are incorporated with additive attention gate units for irrelevant feature elimination, through a U-Net framework with skip connections for information compensation. The performance of fetal heart and lung segmentation indicates the superiority of our method over the existing deep learning based approaches. Our method also shows competitive performance stability during the task of semantic segmentations, showing a promising contribution on ultrasound based prognosis of congenital anomaly in the early intervention, and alleviating the negative effects caused by congenital anomaly.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

scholarly journals Spinal cord segmentation and classification of degenerative disease

2019 ◽  
Vol 10 (3) ◽  
pp. 2426-2432 ◽  
Author(s):  
Arjun ◽  
Kanchana V
Keyword(s):  
Image Processing ◽  
Spinal Cord ◽  
Human Life ◽  
Semantic Segmentation ◽  
Processing Technique ◽  
Degenerative Disease ◽  
Image Processing Technique ◽  
Segmentation Approach ◽  
Work First ◽  
Mri Image

spinal cord plays an important role in human life. In our work, we are using digital image processing technique, the interior part of the human body can be analyzed using MRI, CT and X-RAY etc. Medical image processing technique is extensively used in medical field. In here we are using MRI image to perform our work In our proposed work, we are finding degenerative disease from spinal cord image. In our work first, we are preprocessing the MRI image and locate the degenerative part of the spinal cord, finding the degenerative part using various segmentation approach after that classifying degenerative disease or normal spinal cord using various classification algorithm. For segmentation, we are using an efficient semantic segmentation approach

scholarly journals The Romanian Society of Internal Medicine’s Choosing Wisely Campaign

2019 ◽  
Vol 57 (2) ◽  
pp. 181-194
Author(s):  
Caterina Delcea ◽  
Camelia Badea ◽  
Ciprian Jurcut ◽  
Adrian Purcarea ◽  
Silvia Sovaila ◽  
...  
Keyword(s):  
Internal Medicine ◽  
Scientific Evidence ◽  
Early Mobilization ◽  
Hemoglobin Concentration ◽  
Diagnostic Procedures ◽  
Bed Rest ◽  
Convincing Evidence ◽  
Choosing Wisely ◽  
Motor Vehicles ◽  
European Federation

Abstract Quality of care in medicine is not necessarily proportional to quantity of care and excess is often useless or even more, potentially detrimental to our patients. Adhering to the European Federation of Internal Medicine’s initiative, the Romanian Society of Internal Medicine (SRMI) launched the Choosing Wisely in Internal Medicine Campaign, aiming to cut down diagnostic procedures or therapeutics overused in our country. A Working Group was formed and from 200 published recommendations from previous international campaigns, 36 were voted as most important. These were submitted for voting to the members of the SRMI and posted on a social media platform. After the two voting rounds, the top six recommendations were established. These were: 1. Stop medicines when no further benefit is achieved or the potential harms outweigh the potential benefits for the individual patient. 2. Don’t use antibiotics in patients with recent C. difficile without convincing evidence of need. 3. Don’t regularly prescribe bed rest and inactivity following injury and/or illness unless there is scientific evidence that harm will result from activity. Promote early mobilization. 4. Don’t initiate an antibiotic without an identified indication and a predetermined length of treatment or review date. 5. Don’t prescribe opioids for treatment of chronic or acute pain for sensitive jobs such as operating motor vehicles, forklifts, cranes or other heavy equipment. 6. Transfuse red cells for anemia only if the hemoglobin concentration is less than 7 g/dL or if the patient is hemodynamically unstable or has significant cardiovascular or respiratory comorbidity. Don’t transfuse more units of blood than absolutely necessary.

Signs and Symptoms in Pediatrics

2015 ◽  
Keyword(s):  
Differential Diagnosis ◽  
Abdominal Pain ◽  
Back Pain ◽  
Sleep Disturbances ◽  
Signs And Symptoms ◽  
Diagnostic Procedures ◽  
Heart Murmurs ◽  
Physical Examinations ◽  
Ongoing Care

This convenient handbook is a comprehensive guide to the evaluation and treatment of more than 80 signs and symptoms. It is organized alphabetically, and each entry includes history and physical examinations; causes; differential diagnosis; diagnostic procedures; treatment approaches including when to refer and when to admit; ongoing care and follow-up; and prevention. Contents include: Abdominal pain Anxiety Back pain Chest pain Depression Diarrhea and steatorrhea Dizziness and vertigo Fatigue and weakness Fever Headache Heart murmurs Jaundice Rash Red eye/pink eye Sleep disturbances Speech and language concerns Vomiting Wheezing And more!

scholarly journals A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images

2019 ◽  
Vol 19 (S9) ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Antonio Brunetti ◽  
Giacomo Donato Cascarano ◽  
Andrea Guerriero ◽  
Francesco Pesce ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Kidney Disease ◽  
Autosomal Dominant ◽  
Automatic Segmentation ◽  
Semantic Segmentation ◽  
Magnetic Resonance Images ◽  
Polycystic Kidney ◽  
Autosomal Dominant Polycystic Kidney ◽  
Segmentation Of Images

Abstract Background The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. Methods Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. Results Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. Conclusion The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

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