Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning

A survey on deep learning in medical image analysis

“…Several researchers have shown that designing architectures incorporating unique task-specific properties can obtain better results than straightforward CNNs. Two examples which we encountered several times are multi-view Gao et al (2016d) Frame labeling US CNN 4 class frame classification using transfer learning with pre-trained networks Kumar et al (2016) Frame labeling US CNN 12 standard anatomical planes, CNN extracts features for support vector machine Rajchl et al (2016b) Segmentation with non expert labels MRI CNN Crowd-sourcing annotation efforts to segment brain structures Rajchl et al (2016a) Segmentation given bounding box MRI CNN CNN and CRF for segmentation of structures Ravishankar et al (2016a) Quantification US CNN Hybrid system using CNN and texture features to find abdominal circumference Yu et al (2016b) Left ventricle segmentation US CNN Frame-by-frame segmentation by dynamically fine-tuning CNN to the latest frame Wound segmentation photographs CNN Additional detection of infection risk and healing progress Ypsilantis et al (2015) Chemotherapy response prediction PET CNN CNN outperforms classical radiomics features in patients with esophageal cancer Zheng et al (2015) Carotid artery bifurcation detection CT CNN Two stage detection process, CNNs combined with Haar features Alansary et al (2016) Placenta segmentation MRI CNN 3D multi-stream CNN with extension for motion correction Fritscher et al (2016) Head&Neck tumor segmentation CT CNN 3 orthogonal patches in 2D CNNs, combined with other features Jaumard- Hakoun et al (2016) Tongue contour extraction US RBM Analysis of tongue motion during speech, combines auto-encoders with RBMs Payer et al (2016) Hand landmark detection X-ray CNN Various architectures are compared Quinn et al (2016) Disease detection microscopy CNN Smartphone mounted on microscope detects malaria, tuberculosis & parasite eggs Smistad and Løvstakken (2016) Vessel detection and segmentation US CNN Femoral and carotid vessels analyzed with standard fCNN Twinanda et al (2017) Task recognition in laparoscopy Videos CNN Fine-tuned AlexNet applied to video frames Xu et al (2016c) Cervical dysplasia cervigrams CNN Fine-tuned pre-trained network with added non-imaging features Xue et al (2016) Esophageal microvessel classification Microscopy CNN Simple CNN used for feature extraction Zhang et al (2016a) Image reconstruction CT CNN Reconstructing from limited angle measurements, reducing reconstruction artefacts Lekadir et al (2017) Carotid plaque classification US CNN Simple CNN for characterization of carotid plaque composition in ultrasound …”

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network

“…To segment medical images, different deep-learning approaches have been proposed in 2-D (e.g., left and right ventricles 8 and liver 9 ) and 3-D (e.g., brain tumour 10 and liver 11 ) and have recently been extended to support interactive segmentation in both 2-D and 3-D 12 , 13 . In particular, using 2-D ultrasound images, CNN has been employed to successfully segment deep brain regions, 14 the foetal abdomen, 15 thyroid nodule, 16 foetal left ventricle, 17 and vessels 18 providing a fully automatic approach.…”

Defending Against Child Death: Deep learning‐based diagnosis method for abnormal identification of fetus ultrasound Images

“…Initially, the mask is selected with the size of 3 × 5 × 3 or 7 × 7 and applied into a directional smoothing filter. 28 Then the mean value of the pixels is calculated in all the directions shown in Figure 6, store the results in a matrix R(n), where the size of n is 4. The resultant matrix is calculated using the following equation as, The smoothing filter R 1 (n) is calculated as…”