An Alarm System For Segmentation Algorithm Based On Shape Model

It is usually hard for a learning system to predict correctly on rare events that never occur in the training data, and there is no exception for segmentation algorithms. Meanwhile, manual inspection of each case to locate the failures becomes infeasible due to the trend of large data scale and limited human resource. Therefore, we build an alarm system that will set off alerts when the segmentation result is possibly unsatisfactory, assuming no corresponding ground truth mask is provided. One plausible solution is to project the segmentation results into a low dimensional feature space; then learn classifiers/regressors to predict their qualities. Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data.The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks. During testing, the segmentation results with bad shapes shall not fit the shape prior well, resulting in large loss values. Thus, the VAE is able to evaluate the quality of segmentation result on unseen data, without using ground truth. Finally, we learn a regressor in the one-dimensional feature space to predict the qualities of segmentation results. Our alarm system is evaluated on several recent state-of-art segmentation algorithms for 3D medical segmentation tasks. Compared with other standard quality assessment methods, our system consistently provides more reliable prediction on the qualities of segmentation results.Comment: Accepted to ICCV 2019 (10 pages, 4 figures

COMPRESSIVE SENSING FOR SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY

Spectral domain optical coherence tomography (SD OCT) imaging with high axial resolution and a large imaging depth requires a large number of sampling points in the spectral domain. This requires a high-resolution spectrometer with a large linear array camera which leads to a large amount of k-space measurements and a long data acquisition time that makes the imaging susceptible to unavoidable motion artifact. Furthermore such devices can be expensive and require high-speed electronics. In this dissertation, compressive sensing (CS) SD OCT that reconstructs the images using only a portion of the k-space measurements required by the classical Shannon/Nyquist rate was proposed and studied. Several advanced CS SD OCT algorithms have been developed and evaluated. First, modified non-uniform discrete Fourier transform (MNUDFT) matrix was proposed, which enables CS SD OCT using under-sampled non-linear wavenumber spectral data. Second, the noise reduction using Modified-CS was studied which shows that the averaged Modified-CS SD OCT results in better image quality in terms of SNR, local contrast and contrast to noise ratio (CNR), compared to the classical averaging method. Third, a novel three-dimensional (3D) CS SD OCT sampling pattern and reconstruction procedure was proposed. The novel 3D approach enables efficient volumetric image reconstruction using the k-space measurements under-sampled in all three directions and reduces the amount of required measurements to less than 20% of that required by regular SD OCT. CS SD OCT is commonly solved by an iterative algorithm that requires numerous matrix-vector computation, which is computationally complex and time-consuming if solved on CPU-based systems. However, such computation is ideal for parallel processing with graphics processing unit (GPU) which can significantly reduce its computation time. In this dissertation, real-time CS SD OCT was developed on a conventional desktop computer architecture having three GPUs. The GPU-accelerated CS non-uniform in k-space SD OCT and real-time CS SD OCT with dispersion compensation were also proposed and implemented using the same computer architecture. %Real-time CS SD OCT with dispersion compensation was proposed

Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge

Aorta provides the main blood supply of the body. Screening of aorta with imaging helps for early aortic disease detection and monitoring. In this work, we describe our solution to the Segmentation of the Aorta (SEG.A.231) from 3D CT challenge. We use automated segmentation method Auto3DSeg available in MONAI. Our solution achieves an average Dice score of 0.920 and 95th percentile of the Hausdorff Distance (HD95) of 6.013, which ranks first and wins the SEG.A. 2023 challenge.Comment: MICCAI 2023, SEG.A. 2023 challenge 1st plac

Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge

Kidney and Kidney Tumor Segmentation Challenge (KiTS) 2023 offers a platform for researchers to compare their solutions to segmentation from 3D CT. In this work, we describe our submission to the challenge using automated segmentation of Auto3DSeg available in MONAI. Our solution achieves the average dice of 0.835 and surface dice of 0.723, which ranks first and wins the KiTS 2023 challenge.Comment: MICCAI 2023, KITS 2023 challenge 1st plac

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep supervision. Then an adversarial network is utilized during training process to discriminate the output of DI2IN from ground truth, which further boosts the performance of DI2IN. The proposed method is trained on an annotated dataset of 1000 CT volumes with various different scanning protocols (e.g., contrast and non-contrast, various resolution and position) and large variations in populations (e.g., ages and pathology). Our approach outperforms the state-of-the-art solutions in terms of segmentation accuracy and computing efficiency.Comment: Accepted by MICCAI 201