SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes

Nandamuri, Sumanth; China, Debarghya; Mitra, Pabitra; Sheet, Debdoot

doi:10.48550/arxiv.1901.06920

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Segmentation CNN: The segmentation CNN (net seg (•)) used is an encoder-decoder like architecture [12] with the encoder having layer definitions similar to that of VGG11 [13]. Concatenation of features across matched layers in the encoder and decoder is present in this architecture along with the passing of max pooling indices for up-sampling in the decoder.…”

Section: Methodsmentioning

confidence: 99%

Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging

Sathish

Rajan

Vupputuri

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Self Cite

View full text Add to dashboard Cite

Ischaemic stroke is a medical condition caused by occlusion of blood supply to the brain tissue thus forming a lesion. A lesion is zoned into a core associated with irreversible necrosis typically located at the center of the lesion, while reversible hypoxic changes in the outer regions of the lesion are termed as the penumbra. Early estimation of core and penumbra in ischaemic stroke is crucial for timely intervention with thrombolytic therapy to reverse the damage and restore normalcy. Multisequence magnetic resonance imaging (MRI) is commonly employed for clinical diagnosis. However, a sequence singly has not been found to be sufficiently able to differentiate between core and penumbra, while a combination of sequences is required to determine the extent of the damage. The challenge, however, is that with an increase in the number of sequences, it cognitively taxes the clinician to discover symptomatic biomarkers in these images. In this paper, we present a data-driven fully automated method for estimation of core and penumbra in ischaemic lesions using diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) sequence maps of MRI. The method employs recent developments in convolutional neural networks (CNN) for semantic segmentation in medical images. In the absence of availability of a large amount of labeled data, the CNN is trained using an adversarial approach employing cross-entropy as a segmentation loss along with losses aggregated from three discriminators of which two employ relativistic visual Turing test. This method is experimentally validated on the ISLES-2015 dataset through three-fold cross-validation to obtain with an average Dice score of 0.82 and 0.73 for segmentation of penumbra and core respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging

Sathish

Rajan

Vupputuri

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed method uses SUMNet (Nandamuri et al, 2019) as the base model for segmentation with modifications. We do not use ImageNet pre-trained weights, instead train the model from scratch with the addition of batch-normalization on the ISLES dataset 1 using three MRI sequences available in the SPES subset of the dataset, viz.…”

Section: Methodology and Experimentsmentioning

confidence: 99%

Significance of Residual Learning and Boundary Weighted Loss in Ischaemic Stroke Lesion Segmentation

Rajan,

Sathish,

Sheet

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Radiologists use various imaging modalities to aid in different tasks like diagnosis of disease, lesion visualization, surgical planning and prognostic evaluation. Most of these tasks rely on the the accurate delineation of the anatomical morphology of the organ, lesion or tumor. Deep learning frameworks can be designed to facilitate automated delineation of the region of interest in such cases with high accuracy. Performance of such automated frameworks for medical image segmentation can be improved with efficient integration of information from multiple modalities aided by suitable learning strategies. In this direction, we show the effectiveness of residual network trained adversarially in addition to a boundary weighted loss. The proposed methodology is experimentally verified on the SPES-ISLES 2015 dataset for ischaemic stroke segmentation with an average Dice coefficient of 0.881 for penumbra and 0.877 for core. It was observed that addition of residual connections and boundary weighted loss improved the performance significantly.

show abstract

“…• The network was trained with ADAM optimizer with learning rate 0.001 and decaying with a rate of 0.1 at 7th and 9th epoch. • To achieve multi-modality segmentation using a single framework, a multi-task adversarial learning strategy is employed to train a base segmentation network SUMNet [30] with batch normalization. • Adversarial learning is performed by two auxiliary classifiers, namely C1 and C2, and a discriminator network D.…”

Section: Lachinovmentioning

confidence: 99%

CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation

Kavur,

Gezer,

Barış

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS -Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS provides

show abstract

SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes

Cited by 3 publications

References 0 publications

Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging

Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging

Significance of Residual Learning and Boundary Weighted Loss in Ischaemic Stroke Lesion Segmentation

CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation

Contact Info

Product

Resources

About