Dataset Information

Area Estimation of Deep-Sea Surfaces from Oblique Still Images.

ABSTRACT: Estimating the area of seabed surfaces from pictures or videos is an important problem in seafloor surveys. This task is complex to achieve with moving platforms such as submersibles, towed or remotely operated vehicles (ROV), where the recording camera is typically not static and provides an oblique view of the seafloor. A new method for obtaining seabed surface area estimates is presented here, using the classical set up of two laser devices fixed to the ROV frame projecting two parallel lines over the seabed. By combining lengths measured directly from the image containing the laser lines, the area of seabed surfaces is estimated, as well as the camera's distance to the seabed, pan and tilt angles. The only parameters required are the distance between the parallel laser lines and the camera's horizontal and vertical angles of view. The method was validated with a controlled in situ experiment using a deep-sea ROV, yielding an area estimate error of 1.5%. Further applications and generalizations of the method are discussed, with emphasis on deep-sea applications.

SUBMITTER: Dias FC

PROVIDER: S-EPMC4503614 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Area Estimation of Deep-Sea Surfaces from Oblique Still Images.

Dias Frederico Carvalho FC Gomes-Pereira José J Tojeira Inês I Souto Miguel M Afonso Andreia A Calado António A Madureira Pedro P Campos Aldino A

PloS one 20150715 7

Estimating the area of seabed surfaces from pictures or videos is an important problem in seafloor surveys. This task is complex to achieve with moving platforms such as submersibles, towed or remotely operated vehicles (ROV), where the recording camera is typically not static and provides an oblique view of the seafloor. A new method for obtaining seabed surface area estimates is presented here, using the classical set up of two laser devices fixed to the ROV frame projecting two parallel lines ...[more]

PMID: 26177287

Similar Datasets

Project description:BackgroundMuscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans.MethodsAttenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours.ResultsWe trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%.ConclusionsThis work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer.

Project description:ImportanceThere is no widespread effective treatment to halt the progression of retinitis pigmentosa. Consequently, adequate assessment and estimation of residual visual function are important clinically.ObjectiveTo examine whether deep learning can accurately estimate the visual function of patients with retinitis pigmentosa by using ultra-widefield fundus images obtained on concurrent visits.Design, setting, and participantsData for this multicenter, retrospective, cross-sectional study were collected between January 1, 2012, and December 31, 2018. This study included 695 consecutive patients with retinitis pigmentosa who were examined at 5 institutions. Each of the 3 types of input images-ultra-widefield pseudocolor images, ultra-widefield fundus autofluorescence images, and both ultra-widefield pseudocolor and fundus autofluorescence images-was paired with 1 of the 31 types of ensemble models constructed from 5 deep learning models (Visual Geometry Group-16, Residual Network-50, InceptionV3, DenseNet121, and EfficientNetB0). We used 848, 212, and 214 images for the training, validation, and testing data, respectively. All data from 1 institution were used for the independent testing data. Data analysis was performed from June 7, 2021, to December 5, 2022.Main outcomes and measuresThe mean deviation on the Humphrey field analyzer, central retinal sensitivity, and best-corrected visual acuity were estimated. The image type-ensemble model combination that yielded the smallest mean absolute error was defined as the model with the best estimation accuracy. After removal of the bias of including both eyes with the generalized linear mixed model, correlations between the actual values of the testing data and the estimated values by the best accuracy model were examined by calculating standardized regression coefficients and P values.ResultsThe study included 1274 eyes of 695 patients. A total of 385 patients were female (55.4%), and the mean (SD) age was 53.9 (17.2) years. Among the 3 types of images, the model using ultra-widefield fundus autofluorescence images alone provided the best estimation accuracy for mean deviation, central sensitivity, and visual acuity. Standardized regression coefficients were 0.684 (95% CI, 0.567-0.802) for the mean deviation estimation, 0.697 (95% CI, 0.590-0.804) for the central sensitivity estimation, and 0.309 (95% CI, 0.187-0.430) for the visual acuity estimation (all P < .001).Conclusions and relevanceResults of this study suggest that the visual function estimation in patients with retinitis pigmentosa from ultra-widefield fundus autofluorescence images using deep learning might help assess disease progression objectively. Findings also suggest that deep learning models might monitor the progression of retinitis pigmentosa efficiently during follow-up.

Dataset Information

Area Estimation of Deep-Sea Surfaces from Oblique Still Images.

Publications

Area Estimation of Deep-Sea Surfaces from Oblique Still Images.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets