Optimizing the automatic segmentation of the left ventricle in magnetic resonance images

2005 ◽  
Vol 32 (2) ◽  
pp. 369-375 ◽  
Author(s):  
E. Angelié ◽  
P. J. H. de Koning ◽  
M. G. Danilouchkine ◽  
H. C. van Assen ◽  
G. Koning ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Left Ventricle ◽  
Automatic Segmentation ◽  
Magnetic Resonance Images

scholarly journals Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Jane Tufvesson ◽  
Erik Hedström ◽  
Katarina Steding-Ehrenborg ◽  
Marcus Carlsson ◽  
Håkan Arheden ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Left Ventricle ◽  
Automatic Segmentation ◽  
Magnetic Resonance Images ◽  
Test Set ◽  
Time Resolved ◽  
Manual Delineation ◽  
Mean Differences ◽  
Automatic Removal ◽  
Automatic Algorithm

Introduction.Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion.Methods.Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training setn=40, test setn=50). Manual delineation was reference standard and second observer analysis was performed in a subset (n=25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract.Results.The mean differences between automatic segmentation and manual delineation were EDV −11 mL, ESV 1 mL, EF −3%, and LVM 4 g in the test set.Conclusions.The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.

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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