Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences Using Contextual Memory

Mathai, Tejas Sudharshan; Gorantla, Vijay S.; Galeotti, John

doi:10.1007/978-3-030-32245-8_20

Cited by 7 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our first quantitative experiment, we compared our results against those from expert labels, U-Net [ 10 ], USVS-Net [ 11 ], and HPU-Net [ 19 ] to show that our proposed algorithm is superior to these current segmentation networks and expert labels for needle-and-reverberation-artifact segmentation. The experts tend to label the entire region that might contain artifacts, but they do not differentiate between the reverberations.…”

Section: Methodsmentioning

confidence: 99%

“…U-Net [ 10 ] use an encoder-decoder framework with skip connections between the encoder and the decoder, enabling the network to tend to more fine-grained details. [ 11 ] put Long Short-Term Memory layers (LSTM) and U-Net together, enabling the proposed USVS-Net to excel at identifying ambiguous boundaries and be robust against speckle noises. Attention U-Net is proposed by [ 12 ] to suppress irrelevant regions and has the network focus more on the target structure with different shapes and sizes.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts

2022

Self Cite

View full text Add to dashboard Cite

Objective and Impact Statement. We propose a weakly- and semisupervised, probabilistic needle-and-reverberation-artifact segmentation algorithm to separate the desired tissue-based pixel values from the superimposed artifacts. Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error. Introduction. Ultrasound image quality has continually been improving. However, when needles or other metallic objects are operating inside the tissue, the resulting reverberation artifacts can severely corrupt the surrounding image quality. Such effects are challenging for existing computer vision algorithms for medical image analysis. Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees. The boundaries of such artifacts are ambiguous, leading to disagreement among human experts labeling the artifacts. Methods. Our learning-based framework consists of three parts. The first part is a probabilistic segmentation network to generate the soft labels based on the human labels. These soft labels are input into the second part which is the transform function, where the training labels for the third part are generated. The third part outputs the final masks which quantifies the reverberation artifacts. Results. We demonstrate the applicability of the approach and compare it against other segmentation algorithms. Our method is capable of both differentiating between the reverberations from artifact-free patches and modeling the intensity fall-off in the artifacts. Conclusion. Our method matches state-of-the-art artifact segmentation performance and sets a new standard in estimating the per-pixel contributions of artifact vs underlying anatomy, especially in the immediately adjacent regions between reverberation lines. Our algorithm is also able to improve the performance of downstream image analysis algorithms.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although most of the works in the literature harnessed FCN architectures, a few authors employed recurrent neural networks (RNN) for segmentation tasks (Yi et al, 2019a;Milletari et al, 2018;Mathai et al, 2019) and report good performance. Milletari et al (2018) proposed a novel architecture where the decoding component was long short term memory (LSTM) architecture to obtain multi-scale feature integration.…”

Section: Segmentationmentioning

confidence: 99%

Deep Learning for Chest X-ray Analysis: A Survey

Sogancioglu,

Çallı,

van Ginneken

et al. 2021

Preprint

View full text Add to dashboard Cite

Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Commercially available applications are detailed, and a comprehensive discussion of the current state of the art and potential future directions are provided.

show abstract

“…The ConvLstm can learn the spatiotemporal consistency across the surgical video frames [23] and preserve the spatiotemporal regularity between neighboring frames [24]. Studies proved the efficiency of ConvLstm in learning the temporal variable characteristics from the sequence of frames when incorporated in deep learning networks [25]. When the ConvLstm is included with 3D-CNN, the network is able to extract the short-term and long-term temporal information along with spatial information; producing a feature map that covers the spatiotemporal features of a longer sequence of video frames [26], [27].…”

Section: Introductionmentioning

confidence: 99%

Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos

Ghatwary

Zolgharni

Janan

et al. 2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and early cancerous) can improve the survival rate of the patients. Recent deep learning-based methods for selected types of esophageal abnormality detection from endoscopic images have been proposed. However, no methods have been introduced in the literature to cover the detection from endoscopic videos, detection from challenging frames and detection of more than one esophageal abnormality type. In this paper, we present an efficient method to automatically detect different types of esophageal abnormalities from endoscopic videos. We propose a novel 3D Sequential DenseConvLstm network that extracts spatiotemporal features from the input video. Our network incorporates 3D Convolutional Neural Network (3DCNN) and Convolutional Lstm (ConvLstm) to efficiently learn short and long term spatiotemporal features. The generated feature map is utilized by a region proposal network and ROI pooling layer to produce a bounding box that detects abnormality regions in each frame throughout the video. Finally, we investigate a post-processing method named Frame Search Conditional Random Field (FS-CRF) that improves the overall performance of the model by recovering the missing regions in neighborhood frames within the same clip. We extensively validate our model on an endoscopic video dataset that includes a variety of esophageal abnormalities. Our model achieved high performance using different evaluation metrics showing 93.7% recall, 92.7% precision, and 93.2% F-measure. Moreover, as no results have been reported in the literature for the esophageal abnormality detection from endoscopic videos, to validate the robustness of our model, we have tested the model on a publicly available colonoscopy video dataset, achieving the polyp detection performance in a recall of 81.18%, precision of 96.45% and F-measure 88.16%, compared to the state-of-the-art results of 78.84% recall, 90.51% precision and 84.27% F-measure using the same dataset. This demonstrates that the proposed method can be adapted to different gastrointestinal endoscopic video applications with a promising performance.

show abstract

Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences Using Contextual Memory

Cited by 7 publications

References 16 publications

Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts

Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts

Deep Learning for Chest X-ray Analysis: A Survey

Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos

Contact Info

Product

Resources

About