Preserving Tumor Volumes for Unsupervised Medical Image Registration

Medical image registration is a critical task that estimates the spatial correspondence between pairs of images. However, current traditional and deep-learning-based methods rely on similarity measures to generate a deforming field, which often results in disproportionate volume changes in dissimilar regions, especially in tumor regions. These changes can significantly alter the tumor size and underlying anatomy, which limits the practical use of image registration in clinical diagnosis. To address this issue, we have formulated image registration with tumors as a constraint problem that preserves tumor volumes while maximizing image similarity in other normal regions. Our proposed strategy involves a two-stage process. In the first stage, we use similarity-based registration to identify potential tumor regions by their volume change, generating a soft tumor mask accordingly. In the second stage, we propose a volume-preserving registration with a novel adaptive volume-preserving loss that penalizes the change in size adaptively based on the masks calculated from the previous stage. Our approach balances image similarity and volume preservation in different regions, i.e., normal and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one. This ensures that the tumor volume is preserved during the registration process. We have evaluated our strategy on various datasets and network architectures, demonstrating that our method successfully preserves the tumor volume while achieving comparable registration results with state-of-the-art methods. Our codes is available at: \url{https://dddraxxx.github.io/Volume-Preserving-Registration/}.

Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity

Medical image segmentation is almost the most important pre-processing procedure in computer-aided diagnosis but is also a very challenging task due to the complex shapes of segments and various artifacts caused by medical imaging, (i.e., low-contrast tissues, and non-homogenous textures). In this paper, we propose a simple yet effective segmentation framework that incorporates the geometric prior and contrastive similarity into the weakly-supervised segmentation framework in a loss-based fashion. The proposed geometric prior built on point cloud provides meticulous geometry to the weakly-supervised segmentation proposal, which serves as better supervision than the inherent property of the bounding-box annotation (i.e., height and width). Furthermore, we propose contrastive similarity to encourage organ pixels to gather around in the contrastive embedding space, which helps better distinguish low-contrast tissues. The proposed contrastive embedding space can make up for the poor representation of the conventionally-used gray space. Extensive experiments are conducted to verify the effectiveness and the robustness of the proposed weakly-supervised segmentation framework. The proposed framework is superior to state-of-the-art weakly-supervised methods on the following publicly accessible datasets: LiTS 2017 Challenge, KiTS 2021 Challenge, and LPBA40. We also dissect our method and evaluate the performance of each component.

Weakly-Supervised Camouflaged Object Detection with Scribble Annotations

Existing camouflaged object detection (COD) methods rely heavily on large-scale datasets with pixel-wise annotations. However, due to the ambiguous boundary, it is very time-consuming and labor-intensive to annotate camouflage objects pixel-wisely (which takes ~ 60 minutes per image). In this paper, we propose the first weakly-supervised camouflaged object detection (COD) method, using scribble annotations as supervision. To achieve this, we first construct a scribble-based camouflaged object dataset with 4,040 images and corresponding scribble annotations. It is worth noting that annotating the scribbles used in our dataset takes only ~ 10 seconds per image, which is 360 times faster than per-pixel annotations. However, the network directly using scribble annotations for supervision will fail to localize the boundary of camouflaged objects and tend to have inconsistent predictions since scribble annotations only describe the primary structure of objects without details. To tackle this problem, we propose a novel consistency loss composed of two parts: a reliable cross-view loss to attain reliable consistency over different images, and a soft inside-view loss to maintain consistency inside a single prediction map. Besides, we observe that humans use semantic information to segment regions near boundaries of camouflaged objects. Therefore, we design a feature-guided loss, which includes visual features directly extracted from images and semantically significant features captured by models. Moreover, we propose a novel network that detects camouflaged objects by scribble learning on structural information and semantic relations. Experimental results show that our model outperforms relevant state-of-the-art methods on three COD benchmarks with an average improvement of 11.0% on MAE, 3.2% on S-measure, 2.5% on E-measure and 4.4% on weighted F-measure.