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1. Enhanced U-Net Tool Segmentation using Hybrid Coordinate Representations of Endoscopic Images

   https://doi.org/10.1109/ISMR48346.2021.9661519  

   Huang, Kevin; Chitrakar, Digesh; Jiang, Wenfan; Su, Yun-Hsuan (November 2021, 2021 International Symposium on Medical Robotics (ISMR))

   This paper presents an approach to enhanced endoscopic tool segmentation combining separate pathways utilizing input images in two different coordinate representations. The proposed method examines U-Net convolutional neural networks with input endoscopic images represented via (1) the original rectangular coordinate format alongside (2) a morphological polar coordinate transformation. To maximize information and the breadth of the endoscope frustrum, imaging sensors are oftentimes larger than the image circle. This results in unused border regions. Ideally, the region of interest is proximal to the image center. The above two observations formed the basis for the morphological polar transformation pathway as an augmentation to typical rectangular input image representations. Results indicate that neither of the two investigated coordinate representations consistently yielded better segmentation performance as compared to the other. Improved segmentation can be achieved with a hybrid approach that carefully selects which of the two pathways to be used for individual input images. Towards that end, two binary classifiers were trained to identify, given an input endoscopic image, which of the two coordinate representation segmentation pathways (rectangular or polar), would result in better segmentation performance. Results are promising and suggest marked improvements using a hybrid pathway selection approach compared to either alone. The experiment used to evaluate the proposed hybrid method utilized a dataset consisting of 8360 endoscopic images from real surgery and evaluated segmentation performance with Dice coefficient and Intersection over Union. The results suggest that on-the-fly polar transformation for tool segmentation is useful when paired with the proposed hybrid tool-segmentation approach. 

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2. Iterative Morphological Training Set Decomposition for Endoscopic Tool Segmentation

   https://doi.org/10.1109/AIM55361.2024.10637195  

   Zhu, Yicheng; Wu, Xiaoyi; Tan, Sylvia; Sun, Cuiling; Saha, Sulagna; Su, Yun-Hsuan; Huang, Kevin (July 2024, 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM))

   This paper proposes a modified method for training tool segmentation networks for endoscopic images by parsing training images into two disjoint sets: one for rectangular representations of endoscopic images and one for polar. Previous work [1], [2] demonstrated that certain endoscopic images may be better segmented by a U-Net network trained on the original rectangular representation of images alone, and others performed better with polar representations. This work extends that observation to the training images and seeks to intelligently decompose the aggregate training data into disjoint image sets — one ideal for training a network to segment original, rectangular endoscopic images and the other for training a polar segmentation network. The training set decomposition consists of three stages: (1) initial data split and models, (2) image reallocation and transition mechanisms with retraining, and (3) evaluation. In (2), two separate frameworks for parsing polar vs. rectangular training images were investigated, with three switching metrics utilized in both. Experiments comparatively evaluated the segmentation performance (via Sørenson Dice coefficient) of the in-group and out-of-group images between the set-decomposed models. Results are encouraging, showing improved aggregate in-group Dice scores as well as image sets trending towards convergence. 

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3. Improving Echocardiography Segmentation by Polar Transformation

   Feng, Zishun; Sivak, Joseph; Krishnamurthy, Ashok (September 2022, Statistical Atlas and Computational Models of the Heart)

   Segmentation of echocardiograms plays an essential role in the quantitative analysis of the heart and helps diagnose cardiac diseases. In the recent decade, deep learning-based approaches have significantly improved the performance of echocardiogram segmentation. Most deep learning-based methods assume that the image to be processed is rectangular in shape. However, typically echocardiogram images are formed within a sector of a circle, with a significant region in the overall rectangular image where there is no data, a result of the ultrasound imaging methodology. This large non-imaging region can influence the training of deep neural networks. In this paper, we propose to use polar transformation to help train deep learning algorithms. Using the r-θ transformation, a significant portion of the non-imaging background is removed, allowing the neural network to focus on the heart image. The segmentation model is trained on both x-y and r-θ images. During inference, the predictions from the x-y and r-θ images are combined using max-voting. We verify the efficacy of our method on the CAMUS dataset with a variety of segmentation networks, encoder networks, and loss functions. The experimental results demonstrate the effectiveness and versatility of our proposed method for improving the segmentation results. 

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4. Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation

   https://doi.org/10.3390/s21155163  

   Su, Yun-Hsuan; Jiang, Wenfan; Chitrakar, Digesh; Huang, Kevin; Peng, Haonan; Hannaford, Blake (August 2021, Sensors)

   Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation. 

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5. Structure-Aware Image Segmentation with Homotopy Warping

   Hu, Xiaoling (December 2022, The Thirty-sixth Conference on Neural Information Processing Systems)

   Besides per-pixel accuracy, topological correctness is also crucial for the segmentation of images with fine-scale structures, e.g., satellite images and biomedical images. In this paper, by leveraging the theory of digital topology, we identify pixels in an image that are critical for topology. By focusing on these critical pixels, we propose a new homotopy warping loss to train deep image segmentation networks for better topological accuracy. To efficiently identify these topologically critical pixels, we propose a new algorithm exploiting the distance transform. The proposed algorithm, as well as the loss function, naturally generalize to different topological structures in both 2D and 3D settings. The proposed loss function helps deep nets achieve better performance in terms of topology-aware metrics, outperforming state-of-the-art structure/topology-aware segmentation methods. 

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