Segmentation of colorectal cancerous regions from 3-D magnetic resonance (MR) images is a crucial procedure for radiotherapy. Automatic delineation from 3-D whole volumes is in urgent demand yet very challenging. Drawbacks of existing deep-learning-based methods for this task are two-fold: 1) extensive graphics processing unit (GPU) memory footprint of 3-D tensor limits the trainable volume size, shrinks effective receptive field, and therefore, degrades speed and segmentation performance and 2) in-region segmentation methods supported by region-of-interest (RoI) detection are either blind to global contexts, detail richness compromising, or too expensive for 3-D tasks. To tackle these drawbacks, we propose a novel encoder-decoder-based framework for 3-D whole volume segmentation, referred to as 3-D RoI-aware U-Net (3-D RU-Net). 3-D RU-Net fully utilizes the global contexts covering large effective receptive fields. Specifically, the proposed model consists of a global image encoder for global understanding-based RoI localization, and a local region decoder that operates on pyramid-shaped in-region global features, which is GPU memory efficient and thereby enables training and prediction with large 3-D whole volumes. To facilitate the global-to-local learning procedure and enhance contour detail richness, we designed a dice-based multitask hybrid loss function. The efficiency of the proposed framework enables an extensive model ensemble for further performance gain at acceptable extra computational costs. Over a dataset of 64 T2-weighted MR images, the experimental results of four-fold cross-validation show that our method achieved 75.5% dice similarity coefficient (DSC) in 0.61 s per volume on a GPU, which significantly outperforms competing methods in terms of accuracy and efficiency. The code is publicly available.

Automatic rib fracture recognition from chest X-ray images is clinically important yet challenging due to weak saliency of fractures. Weakly Supervised Learning (WSL) models recognize fractures by learning from large-scale image-level labels. In WSL, Class Activation Maps (CAMs) are considered to provide spatial interpretations on classification decisions. However, the high-responding regions, namely Supporting Regions of CAMs may erroneously lock to regions irrelevant to fractures, which thereby raises concerns on the reliability of WSL models for clinical applications. Currently available Mixed Supervised Learning (MSL) models utilize object-level labels to assist fitting WSL-derived CAMs. However, as a prerequisite of MSL, the large quantity of precisely delineated labels is rarely available for rib fracture tasks. To address these problems, this paper proposes a novel MSL framework. Firstly, by embedding the adversarial classification learning into WSL frameworks, the proposed Biased Correlation Decoupling and Instance Separation Enhancing strategies guide CAMs to true fractures indirectly. The CAM guidance is insensitive to shape and size variations of object descriptions, thereby enables robust learning from bounding boxes. Secondly, to further minimize annotation cost in MSL, a CAM-based Active Learning strategy is proposed to recognize and annotate samples whose Supporting Regions cannot be confidently localized. Consequently, the quantity demand of object-level labels can be reduced without compromising the performance. Over a chest X-ray rib-fracture dataset of 10966 images, the experimental results show that our method produces rational Supporting Regions to interpret its classification decisions and outperforms competing methods at an expense of annotating 20% of the positive samples with bounding boxes.

Colorectal cancer is among the leading cause of cancer-related mortalities. The cancerous regions are conventionally delineated from 3D magnetic resonance images in a voxel-wise way for radiotherapy. To ease the manual labeling procedure, which is laborious and time-consuming, automatic segmentation methods are highly demanded in clinical practice. However, it is a challenging task due to class imbalance and low-contrast appearance of cancerous regions, as well as the hard mimics from complex peritumoral areas. In this paper, we propose a volume-to-volume fully convolutional network architecture effectively trained with hybrid loss, referred as HL-FCN, to automatically segment colorectal cancer regions. Specifically, a novel Dice-based hybrid loss is designed under a multi-task learning framework to tackle the class-imbalance issue and hence improve the discrimination capability. Furthermore, a multi-resolution model ensemble strategy is developed to suppress false positives while preserving boundary details. Our method has been extensively validated on 64 cancerous cases using four-fold cross-validation, outperforming state-of-the-art methods by a significant margin.