Deep Mouse: An End-to-End Auto-Context Refinement Framework for Brain Ventricle & Body Segmentation in Embryonic Mice Ultrasound Volumes

Abstract

High-frequency ultrasound (HFU) is well suited for imaging embryonic mice due to its noninvasive and real-time characteristics. However, manual segmentation of the brain ventricles (BVs) and body requires substantial time and expertise. This work proposes a novel deep learning based endto-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume ≈1000x faster) while slightly improves the segmentation accuracy over the previous methods using slidewindow approaches.

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

“…Following the review of the title and abstract and, upon necessity, the full text further 187 records were rejected. After the reviewing process, a total of 36 original papers [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] and 20 conference proceedings [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ,…”

“…This last paragraph collects 13 papers [ 28 , 32 , 33 , 36 , 46 , 57 , 58 , 71 , 74 , 78 , 79 , 80 , 82 ] that proposed heterogeneous US-based applications adopting DL systems. In particular, papers focused on embryo segmentation [ 28 , 57 , 79 , 80 , 82 ] and embryo reconstruction [ 78 ] or on the vascularisation of breast cancer tissue [ 32 , 46 ], lymph node [ 58 ], hind limb [ 33 ], chorioallantoic membrane [ 36 ] and lung [ 71 , 74 ].…”

3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

“…Recent work has made extensive use of 3D CNNs [5], [6], [7], [8], [9], [10], [11], [12]. 3D versions of Deep Learning architectures like VGGNet( [13]) [14], Faster R-CNN( [15]) [16], [17] and V-Net ( [18]) [19], [20] are very popular. Although most approaches today rely on CNNs, more traditional approaches are still present.…”

“…First, the entire image is viewed to roughly locate one or more targets. The resulting sub-optimal segmentation is then utilized to place a BB around the area of interest [32], [33], [34], [29], [5], [31], [19], [20], [39]. Similar to a coarse-segmentation approach, H. Roth et al (2018) [38] implement a 2D pixel-wise probability detection in every image plane direction to obtain confidence heatmaps, which are then used to generate a 3D BB.…”

PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation

“…Inspired by a fully convolutional network structure [18,19,[27][28][29] and a residual network structure [30], in this paper we present a novel deep neural network model for the polyp segmentation task: the parallel residual atrous pyramid network (PRAPNet). We used the residual network as our backbone network for extracting the common features.…”