Tumor segmentation in Computed Tomography (CT) images is a crucial step in image-guided surgery. However, low-contrast CT images impede the performance of subsequent segmentation tasks. Contrast enhancement is then used as a preprocessing step to highlight the relevant structures, thus facilitating not only medical diagnosis but also image segmentation with higher accuracy. In this paper, we propose a goal-oriented contrast enhancement method to improve tumor segmentation performance. The proposed method is based on two concepts, namely guided image enhancement and image quality control through an optimization scheme. The proposed OPTimized Guided Contrast Enhancement (OPTGCE) scheme exploits both contextual information from the guidance image and structural information from the input image in a two-step process. The first step consists of applying a two-dimensional histogram specification exploiting contextual information in the corresponding guidance image, i.e. Magnetic Resonance Image (MRI). In the second step, an optimization scheme using a structural similarity measure to preserve the structural information of the original image is performed. To the best of our knowledge, this kind of contrast enhancement optimization scheme using cross-modal guidance is proposed for the first time in the medical imaging context. The experimental results obtained on real data demonstrate the effectiveness of the method in terms of enhancement and segmentation quality in comparison to some state-of-the-art methods based on the histogram.

Accurate segmentation of the liver and tumors from CT volumes is crucial for hepatocellular carcinoma diagnosis and pre-operative resection planning. Despite advances in deep learning-based methods for abdominal CT images, fully-automated segmentation remains challenging due to class imbalance and structural variations, often requiring cascaded approaches that incur significant computational costs. In this paper, we present the Dual-Encoder Double Concatenation Network (DEDC-Net) for simultaneous segmentation of the liver and its tumors. DEDC-Net leverages both residual and skip connections to enhance feature reuse and optimize performance in liver and tumor segmentation tasks. Extensive qualitative and quantitative experiments on the LiTS dataset demonstrate that DEDC-Net outperforms existing state-of-the-art liver segmentation methods. An ablation study was conducted to evaluate different encoder backbones - specifically VGG19 and ResNet - and the impact of incorporating an attention mechanism. Our results indicate that DEDC-Net, without any additional attention gates, achieves a superior mean Dice Score (DS) of 0.898 for liver segmentation. Moreover, integrating residual connections into one encoder yielded the highest DS for tumor segmentation tasks. The robustness of our proposed network was further validated on two additional, unseen CT datasets: IDCARDb-01 and COMET. Our model demonstrated superior lesion segmentation capabilities, particularly on IRCADb-01, achieving a DS of 0.629. The code implementation is publicly available at this website.

Cross-modal medical imaging techniques are predominantly being used in the clinical suite. The ensemble learning methods using cross-modal medical imaging adds reliability to several medical image analysis tasks. Motivated by the performance of deep learning in several medical imaging tasks, a deep learning-based denoising method Cross-Modality Guided Denoising Network $CMGDNet$ for removing Rician noise in T1-weighted (T1-w) Magnetic Resonance Images (MRI) is proposed in this paper. $CMGDNet$ uses a guidance image, which is a cross-modal (T2-w) image of better perceptual quality to guide the model in denoising its noisy T1-w counterpart. This cross-modal combination allows the network to exploit complementary information existing in both images and therefore improve the learning capability of the model. The proposed framework consists of two components: Paired Hierarchical Learning (PHL) module and Cross-Modal Assisted Reconstruction (CMAR) module. PHL module uses Siamese network to extract hierarchical features from dual images, which are then combined in a densely connected manner in the CMAR module to finally reconstruct the image. The impact of using registered guidance data is investigated in removing noise as well as retaining structural similarity with the original image. Several experiments were conducted on two publicly available brain imaging datasets available on the IXI database. The quantitative assessment using Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) demonstrates that the proposed method exhibits 4.7% and 2.3% gain (average), respectively, in SSIM and FSIM values compared to other state-of-the-art denoising methods that do not integrate cross-modal image information in removing various levels of noise.