Deidentifying MRI Data Domain by Iterative Backpropagation

Parreño, Mario; Paredes, Roberto; Albiol, Alberto

doi:10.1007/978-3-030-68107-4_28

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Finally, for edge enhancement, which allowed for improved delineation, the Contrast Limited Adaptive Histogram Equalization (CLAHE) was used. -CNN architecture, see Figure 4: The deep learning model proposed was based on the design of Parreño, 20 where it is described that due to its fast and accurate segmentation of medical images, the best architecture for the problem posed is a U-Net. 21 The encoder, on its part, is composed of the Resnet-34 feature extractor, accompanied by intermediate Spatial and Channel Compression and Excitation blocks.…”

Section: Stage 1: Convolutional Neural Networkmentioning

confidence: 99%

VENTSEG: efficient open source framework for ventricular segmentation

Leon

Herrera

Urbina

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

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Despite advances in deep learning methods aimed at cardiac ventricular segmentation, most algorithms have drawbacks due to low prediction accuracy with images from different MR scans to those trained. It leads to a process that requires time-consuming correction by technicians or specialists. The time in this process is significant mainly due to the large number of image sets to be processed. The lack of description of the algorithms has not allowed repeatability, while commercial software is difficult to access for clinical use or research. However, in cardiac segmentation research, several solutions have already been proposed. This paper presents an opensource cardiac functionality segmentation and evaluation framework, which contemplates a diverse database for network training, a multi domain network architecture that allows model generalization, and pre-and postprocessing algorithms that improve prediction results. The prediction evaluation of the framework shows that Ventseg is 3.66% superior to the trained model and the similarity percentages in the tested MR scores are over 84%. On the other hand, the inter-observer variability analysis, with anonymized data, shows in the different metrics that Ventseg is on par with cardiac segmentation specialists. Finally, the efficiency calculated in an intra-observer test indicates that our framework reduces manual segmentation time by approximately 80%.

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Section: Stage 1: Convolutional Neural Networkmentioning

confidence: 99%

VENTSEG: efficient open source framework for ventricular segmentation

Leon

Herrera

Urbina

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

View full text Add to dashboard Cite

show abstract

“…The resulting augmented data was used to train a 3D U-Net model. Parreño et al [27] used a Resnet-34 model to classify the image and appropriately adapt it to a known domain using iterative backpropagation. The resulting adapted images were then segmented using U-Net.…”

Section: Prior Workmentioning

confidence: 99%

Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images

Chhabra¹,

Gagan²,

Kumar³

2022

Preprint

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Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. The ejection fraction is a measurement that expresses the percentage of blood leaving the heart with each contraction. Cardiologists often use ejection fraction to determine one's cardiac function. We propose multiscale template matching technique for detection and an elliptical active disc for automated segmentation of the left ventricle in MR images. The elliptical active disc optimizes the local energy function with respect to its five free parameters which define the disc. Gradient descent is used to minimize the energy function along with Green's theorem to optimize the computation expenses. We report validations on 320 scans containing 5,273 annotated slices which are publicly available through the Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Segmentation (M&Ms) Challenge. We achieved successful localization of the left ventricle in 89.63% of the cases and a Dice coefficient of 0.873 on diastole slices and 0.770 on systole slices. The proposed technique is based on traditional image processing techniques with a performance on par with the deep learning techniques.

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