Dr. Ngan T. Le Profile

Dr. Ngan T. Le

at Univ of Arkansas

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Proceedings Article | 15 March 2022 Paper

Invertible residual network with regularization for effective volumetric segmentation

Kashu Yamazaki, Vidhiwar Singh Rathour, T. Hoang Ngan Le

Proceedings Volume 11596, 1159610 (2022) https://doi.org/10.1117/12.2581396

KEYWORDS: Image segmentation, 3D modeling, Computer programming, Tumors, Network architectures, Neural networks, Data modeling, Convolutional neural networks, Spatial resolution, Performance modeling

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Deep Convolutional Neural Networks (CNNs) i.e. Residual Networks (ResNets) have been used successfully for many computer vision tasks, but are difficult to scale to 3D volumetric medical data. Memory is increasingly often the bottleneck when training 3D Convolutional Neural Networks (CNNs). Recently, invertible neural networks have been applied to significantly reduce activation memory footprint when training neural networks with backpropagation thanks to the invertible functions that allow retrieving input from its output without storing intermediate activations in memory to perform the backpropagation. Among many successful network architectures, 3D Unet1 has been established as a standard architecture for volumetric medical segmentation. Thus, we choose 3D Unet as a baseline for a non-invertible network and we then extend it with the invertible residual network. In this paper, we proposed two versions of invertible Residual Network, namely Partially Invertible Residual Network (Partially-InvRes) and Fully Invertible Residual Network (Fully-InvRes). In Partially-InvRes, the invertible residual layer is defined by a technique called additive coupling2 whereas in Fully-InvRes, both invertible upsampling and downsampling operations are learned based on squeezing (known as pixel shuffle).³ Furthermore, to avoid the overfitting problem because of less training data, a variational auto-encoder (VAE) branch is added to reconstruct the input volumetric data itself. Our results indicate that by using partially/fully invertible networks as the central workhorse in volumetric segmentation, we not only reduce memory overhead but also achieve compatible segmentation performance compared against the non-invertible 3D Unet. We have demonstrated the proposed networks on various volumetric datasets such as iSeg 2019⁴ and BraTS 2020.⁵

Proceedings Article | 13 March 2017 Presentation + Paper

An automatic cells detection and segmentation

Ligong Han, T. Hoang Ngan Le, Marios Savvides

Proceedings Volume 10137, 101370W (2017) https://doi.org/10.1117/12.2254068

KEYWORDS: Neodymium, Blood, Image segmentation, RGB color model, Detection and tracking algorithms, Principal component analysis, Image filtering, Visualization, Pathology, CMYK color model

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This paper presents an end-to-end framework for automatically detecting and segmenting blood cells including normal red blood cells (RBCs), connected RBCs, abnormal RBCs (i.e. tear drop, burr cell, helmet, etc.) and white blood cells (WBCs). Our proposed system contains several components to solve different problems regarding RBCs and WBCs. We first design a novel blood cell color representation which is able to emphasize the RBCs and WBCs in separate channels. Template matching technique is then employed to individually detect RBCs and WBCs in our proposed representation. In order to automatically segment the RBCs and nuclei from WBCs, we develop an adaptive level set-based segmentation method which makes use of both local and global information. The detected and segmented RBCs, however, can be a single RBC, a connected RBC or an abnormal RBC. Therefore, we first separate and reconstruct RBCs from the connected RBCs by our suggested modified template matching. Shape matching by inner distance is later used to classify the abnormal RBCs from the normal RBCs. Our proposed method has been tested and evaluated on different images from ALL-IDB,¹⁰ WebPath,²⁴ UPMC,²³ Flicker datasets, and the one used by Mohamed et al.¹⁴ The precision and recall of RBCs detection are 98.43% and 94.99% respectively, whereas those of WBCs detection are 99.12% and 99.12%. The F-measure of our proposed WBCs segmentation gets up to 95.8%.

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