Automatic image segmentation is an essential step for many medical image analysis applications, include computer-aided radiation therapy, disease diagnosis, and treatment effect evaluation. One of the major challenges for this task is the blurry nature of medical images (e.g., CT, MR, and microscopic images), which can often result in low-contrast and vanishing boundaries. With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder-decoder deep architectures. However, in many applications (with adjacent targets in blurry images), these models often fail to accurately locate complex boundaries and properly segment tiny isolated parts. In this paper, we aim to provide a method for blurry medical image segmentation and argue that skip connections are not enough to help accurately locate indistinct boundaries. Accordingly, we propose a novel high-resolution multi-scale encoder-decoder network (HMEDN), in which multi-scale dense connections are introduced for the encoder-decoder structure to finely exploit comprehensive semantic information. Besides skip connections, extra deeply supervised high-resolution pathways (comprised of densely connected dilated convolutions) are integrated to collect high-resolution semantic information for accurate boundary localization. These pathways are paired with a difficulty-guided cross-entropy loss function and a contour regression task to enhance the quality of boundary detection. The extensive experiments on a pelvic CT image dataset, a multi-modal brain tumor dataset, and a cell segmentation dataset show the effectiveness of our method for 2D/3D semantic segmentation and 2D instance segmentation, respectively. Our experimental results also show that besides increasing the network complexity, raising the resolution of semantic feature maps can largely affect the overall model performance. For different tasks, finding a balance between these two factors can further improve the performance of the corresponding network.
- Image segmentation,
- low-contrast image,
- high-resolution pathway
- Image segmentation,
- Task analysis,
- Computed tomography,
- Medical diagnostic imaging
MEDICAL image analysis develops methods for solving problems pertaining to medical images and their use for clinical care. Among these methods and applications, automatic image segmentation plays an important role in therapy planning , disease diagnosis [2–4], and pathology learning  strategies. For example, in image-guided disease diagnosis for brain cancer, accurately segmented masks of sub-components of a brain tumor enable the physicians to estimate the volume of gliomas (of different grade) and then conduct progression monitoring, radiotherapy planning, outcome assessment, and follow-up studies . The primary challenges for medical image segmentation mainly lie in three aspects. For the ease of understanding, pelvic CT images are selected as an example for illustration, similar conditions also exist in many other segmentation tasks, including a brain tumor and cell segmentation. (1) Complex boundary interactions: The main target organs of pelvic CT image segmentation are the three adjacent soft tissues, i.e., prostate, bladder, and rectum. Since these organs are adjacent to each other and their shapes and scales can be changed easily and significantly by different amounts of urine or bowel gas inside the organs, the boundary interaction of these organs can be complicated. (2) Large appearance variation: The appearance of main pelvic organs may change dramatically for the cases with or without bowel gas, contrast agents, fiducial markers, and metal implants. (3) Low tissue contrast: CT images, especially those from the pelvic area, have blurry and vanishing boundaries (see Fig. 1).
In this paper, we proposed a high-resolution multi-scale encoder-decoder network (HMEDN) to segment medical images, especially for the challenging cases with blurry and vanishing boundaries caused by low tissue contrast. In this network, three kinds of pathways (i.e., skip pathways, distilling pathways, and high-resolution pathways) were integrated to extract meaningful features that capture accurate location and semantic information. Specifically, in the distilling pathway, both U-Net structure and HED structure were utilized to capture comprehensive multi-scale information. In the high-resolution pathway, the densely connected residual dilated blocks were adopted to extract location accurate semantic information for the vague boundary localization. Moreover, to further improve the boundary localization accuracy and the performance of the network on the relatively “hard” regions, we added a contour regression task and a difficulty-guided cross-entropy loss to the network. Extensive experiments indicated the superior performance and good generality of our designed network. Through the experiments, we made several observations: (1) Skip connections, which are usually adopted in the encoder-decoder networks, are not enough for detecting the blurry and vanishing boundaries in medical images. (2) Finding a good balance between semantic feature resolution and the network complexity is an important factor for the segmentation performance, especially when small and complicated structures are being segmented in blurry images. Observing the failed samples of our algorithm, we found that the algorithm fails in cases where the boundaries are totally invisible due to significant amounts of noise incurred by low dose, metal, and motion artifacts, and so forth. To solve these problems, in the future we will combine our algorithm with shape-based segmentation methods and incorporate more robust shape and structural information of target organs.
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FULL Paper PDF file:High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation
High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation
in IEEE Transactions on Image Processing, vol. 29, pp. 461-475, 2020
PDF reference and original file: Click here