Image Segmentation Using Deep Learning Techniques in Medical Images

Mittal, Mamta; Arora, Maanak; Pandey, Tushar; Goyal, Lalit Mohan

doi:10.1007/978-981-15-1100-4_3

Cited by 35 publications

(15 citation statements)

References 20 publications

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“…First, the manual segmentations of the training set and for the external validation were performed only once by a single operator, and a quantitative assessment of the reliability and quality of the ground truth data could therefore not be performed. Besides, a single network architecture was employed as a basis for the development and optimization of the model; while U-Net is largely considered as a state-of-the-art solution for the segmentation of medical images [26], other solutions such as for example generative adversarial networks may be worthy of investigation. Finally, the subjects recruited for the external validation were in a relatively low number and, while they covered a rather wide range of age and body sizes, were all asymptomatic.…”

Section: Discussionmentioning

confidence: 99%

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

Niemeyer

Zanker

Jonas

et al. 2021

Preprint

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Purpose. Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset. Methods. A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers. Results. The Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network. Cross-sectional area and fat fraction of the muscles were in agreement with published data. Conclusions. The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in axial MRI scans in an accurate and fully automated manner, and is therefore a suitable tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

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Section: Discussionmentioning

confidence: 99%

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

Niemeyer

Zanker

Jonas

et al. 2021

Preprint

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“…Afchar et al [21] presented two networks, both having a low number of layers so that they could focus on the mesoscopic properties of images, which made their forgery detection technique fast and reliable. Image segmentation has been found useful for various purposes [22][23][24]. In addition, specific techniques utilized in the medical domain and watermarking [25][26][27] could be used as a reference for forgery detection.…”

Section: Related Workmentioning

confidence: 99%

DeepFake Video Detection: A Time-Distributed Approach

et al. 2020

Self Cite

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Recent developments in machine learning algorithms have led to the generation of forged videos having remarkable quality, which are indistinguishable from real videos. This can fatally affect the way in which one perceives the information available digitally. Thus, this paper aims to efficiently and holistically detect manipulated videos generated using DeepFake, which is the most effective deep learning powered technique developed so far by the researchers. Arduous efforts have been put to detect the forgery in still images, but the authors leveraged the spatio-temporal features of the videos by taking sequences of frames as input to the model. Furthermore, the authors have proposed an architecture which took advantage of lower-level features around regions of interest as well as discrepancies across multiple frames. Experiments have been performed on the Deep Fake Detection Challenge dataset of ≈ 470 GB in size, and it has been observed that the proposed approach yielded a test accuracy score of 97.6%.

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“…This is quite similar to what had been done in CNNs for a single neural layer. As CNNs have been used in medical informatics [12][13][14], CapsNet have also been increasingly employed in medical tasks such as brain tumor segmentation and classification [15] and works such as lung cancer detection [16] and blood cell classification [17].…”

Section: Introductionmentioning

confidence: 99%

Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images

Mittal

Kumar

Mittal³

et al. 2020

Sensors

Self Cite

119

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An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.

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Image Segmentation Using Deep Learning Techniques in Medical Images

Cited by 35 publications

References 20 publications

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

DeepFake Video Detection: A Time-Distributed Approach

Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images

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