Dense Volume-to-Volume Vascular Boundary Detection

Abstract

Abstract. In this work, we present a novel 3D-Convolutional Neural Network (CNN) architecture called I2I-3D that predicts boundary location in volumetric data. Our fine-to-fine, deeply supervised framework addresses three critical issues to 3D boundary detection: (1) e cient, holistic, end-to-end volumetric label training and prediction (2) precise voxel-level prediction to capture fine scale structures prevalent in medical data and (3) directed multi-scale, multi-level feature learning. We evaluate our approach on a dataset consisting of 93 medical image volumes with a wide variety of anatomical regions and vascular structures. In the process, we also introduce HED-3D, a 3D extension of the state-of-theart 2D edge detector (HED). We show that our deep learning approach out-performs, the current state-of-the-art in 3D vascular boundary detection (structured forests 3D), by a large margin, as well as HED applied to slices, and HED-3D while successfully localizing fine structures. With our approach, boundary detection takes about one minute on a typical 512x512x512 volume.

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

“…In [33], the FCN approach is extended to perform 3D vascular segmentation in cardiovascular and pulmonary vessels in MRI and CT volumes, respectively.…”

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MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

“…U-Net has been showing impressive potential in segmenting medical images, even with a scarce amount of labeled training data, to an extent that it has become the de-facto standard in medical image segmentation [22]. U-Net and U-Net like models have been successfully used in segmenting biomedical images of neuronal structures [24], liver [25], skin lesion [26], colon histology [27], kidney [28], vascular boundary [29], lung nodule [30], prostate [31], etc. and the list goes on.…”

Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes

“…The down-sampling phase detects features, while the up-sampling phase accurately localizes the detected features. Such combination is proven essential in recent literature [11,4,12] to acquire precise segmentation.…”