scholarly journals EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames

2021 ◽  
Vol 1 (1) ◽  
pp. 14-16
Author(s):  
Debapriya Banik ◽  
Kaushiki Roy ◽  
Debotosh Bhattacharjee
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Surgical Instruments ◽  
Test Dataset ◽  
Segmentation Task

This paper addresses the Instrument Segmentation Task, a subtask for the “MedAI: Transparency in Medical Image Segmentation” challenge. To accomplish the subtask, our team “Med_Seg_JU” has proposed a deep learning-based framework, namely “EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames”. The proposed framework is inspired by the M-Net architecture. In this architecture, we have incorporated the EfficientNet B3 module with U-Net as the backbone. Our proposed method obtained a JC of 0.8205, DSC of 0.8632, PRE of 0.8464, REC of 0.9005, F1 of 0.8632, and ACC of 0.9799 as evaluated by the challenge organizers on a separate test dataset. These results justify the efficacy of our proposed method in the segmentation of the surgical instruments.

scholarly journals Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 844
Author(s):  
Baixin Jin ◽  
Pingping Liu ◽  
Peng Wang ◽  
Lida Shi ◽  
Jing Zhao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Optic Disc ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Attention Mechanism ◽  
Cross Entropy ◽  
Segmentation Task

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.

scholarly journals HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2107
Author(s):  
Xin Wei ◽  
Huan Wan ◽  
Fanghua Ye ◽  
Weidong Min
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Loss Function ◽  
Network Structure ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Training Mode ◽  
Cooperative Training

In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.

A Study on Deep Learning Models for Medical Image Segmentation

2021 ◽  
pp. 161-174
Author(s):  
Pashupati Bhatt ◽  
Ashok Kumar Sahoo ◽  
Saumitra Chattopadhyay ◽  
Chandradeep Bhatt
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Learning Models

scholarly journals Robustness study of noisy annotation in deep learning based medical image segmentation

2020 ◽  
Vol 65 (17) ◽  
pp. 175007
Author(s):  
Shaode Yu ◽  
Mingli Chen ◽  
Erlei Zhang ◽  
Junjie Wu ◽  
Hang Yu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Segmentation

scholarly journals Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning

2019 ◽  
Vol 38 (11) ◽  
pp. 2642-2653 ◽  
Author(s):  
Jamshid Sourati ◽  
Ali Gholipour ◽  
Jennifer G. Dy ◽  
Xavier Tomas-Fernandez ◽  
Sila Kurugol ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Fisher Information ◽  
Medical Image ◽  
Medical Image Segmentation

scholarly journals Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

2019 ◽  
Vol 33 ◽  
pp. 865-872 ◽  
Author(s):  
Cheng Chen ◽  
Qi Dou ◽  
Hao Chen ◽  
Jing Qin ◽  
Pheng-Ann Heng
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
Target Domain ◽  
Learning Procedure ◽  
The Neural Networks ◽  
Feature Adaptation ◽  
Segmentation Task

This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of crossmodality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.

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