Liver cancer is the sixth most common cancer in the world and the fourth leading cause of cancer mortality. In unresectable liver cancers, especially hepatocellular carcinoma (HCC), transarterial radioembolisation (TARE) can be considered for treatment. TARE treatment involves a contrast-enhanced magnetic resonance imaging (CE-MRI) exam performed beforehand to delineate the liver and tumour(s) in order to perform dosimetry calculation. Due to the significant amount of time and expertise required to perform the delineation process, there is a strong need for automation. Unfortunately, the lack of publicly available CE-MRI datasets with liver tumour annotations has hindered the development of fully automatic solutions for liver and tumour segmentation. The “Tumour and Liver Automatic Segmentation” (ATLAS) dataset that we present consists of 90 liver-focused CE-MRI covering the entire liver of 90 patients with unresectable HCC, along with 90 liver and liver tumour segmentation masks. To the best of our knowledge, the ATLAS dataset is the first public dataset providing CE-MRI of HCC with annotations. The public availability of this dataset should greatly facilitate the development of automated tools designed to optimise the delineation process, which is essential for treatment planning in liver cancer patients.

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

In medical imaging, accurate segmentation is crucial to improving diagnosis, treatment, or both. However, navigating the multitude of available architectures for automatic segmentation can be overwhelming, making it challenging to determine the appropriate type of architecture and tune the most crucial parameters during dataset optimisation. To address this problem, we examined and refined seven distinct architectures for segmenting the liver, as well as liver tumours, with a restricted training collection of 60 3D contrast-enhanced magnetic resonance images (CE-MRI) from the ATLAS dataset. Included in these architectures are convolutional neural networks (CNNs), transformers, and hybrid CNN/transformer architectures. Bayesian search techniques were used for hyperparameter tuning to hasten convergence to the optimal parameter mixes while also minimising the number of trained models. It was unexpected that hybrid models, which typically exhibit superior performance on larger datasets, would exhibit comparable performance to CNNs. The optimisation of parameters contributed to better segmentations, resulting in an average increase of 1.7% and 5.0% in liver and tumour segmentation Dice coefficients, respectively. In conclusion, the findings of this study indicate that hybrid CNN/transformer architectures may serve as a practical substitute for CNNs even in small datasets. This underscores the significance of hyperparameter optimisation.

In medical imaging, accurate segmentation is crucial to improving diagnosis, treatment, or both. However, navigating the multitude of available architectures for automatic segmentation can be overwhelming, making it challengingto determine the appropriate type of architecture and tune the most crucial parameters during dataset optimisation.To address this problem, we examined and refined seven distinct architectures for segmenting the liver, as well as liver tumours, with a restricted training collection of 60 3D contrast-enhanced magnetic resonance images (CE-MRI) from the ATLAS dataset. Included in these architectures are convolutional neural networks (CNNs), transformers, and hybrid CNN/transformer architectures. Bayesian search techniques were used for hyperparameter tuning to hasten convergence to the optimal parameter mixes while also minimising the number of trained models. It was unexpected that hybrid models, which typically exhibit superior performance on larger datasets, would exhibit comparable performance to CNNs. The optimisation of parameters contributed to better segmentations, resulting in an average increase of 1.7% and 5.0% in liver and tumour segmentation Dice coefficients, respectively.In conclusion, the findings of this study indicate that hybrid CNN/transformer architectures may serve as a practical substitute for CNNs even in small datasets. This underscores the significance of hyperparameter optimisation.

Given the increasing data requirements of deep learning models and the scarcity of medical imaging data, new data augmentation techniques are receiving particular attention. This paper explores the subfield of tumour synthesis within medical image generation, focusing on the development of synthetic tumours in MR images. This study introduces a novel tumour generation method using diffusion models, designed to inpaint visually convincing 3D synthetic liver tumours into real MRI volumes while generating the corresponding masks using simplex deformation. This approach has been employed successfully to inpaint images with 1000 synthetic tumours. Furthermore, it has shown significant performance improvements when applied in image segmentation tasks. In particular, our method improved the Dice coefficient by 6.7 points on the ATLAS test set without relying on external data. When combined with a pseudo-annotated external dataset, the improvement increased to 10 points. This study not only demonstrates the ability to segment tumours but also paves the way for various synthetic data-based applications in medical imaging.

Background: Selective internal radiation therapy (SIRT) increasingly relies on accurate magnetic resonance imaging (MRI) to computed tomography (CT) registration and accurate liver and tumour segmentation, for effective pre-treatment planning. This study evaluates the impact of automatic registration and segmentation techniques on dosimetry calculations for SIRT treatment planning. It compares semi-automatic and automatic registration, as well as manual and automatic deep learning-based segmentation. Methods: Pre-treatment data from 90 patients with hepatocellular carcinoma were analysed. The dataset consisted of contrast-enhanced T1-weighted MRI scans with manually delineated liver and tumour volumes of interest (VOIs), as well as single-photon emission computed tomography (SPECT)/CT scans with manually or semi-automatically delineated liver VOI. The clinical routine pipeline, which involves semi-automatic registration and manual/semi-automatic segmentation, was used as the baseline pipeline and compared to experimental pipelines that use intensity-based deformable automatic registration or deep learning-based automatic segmentation. Dosimetric accuracy was assessed via metrics such as the mean absorbed dose, the minimum dose received by 70% of the volume (D70), and inverted cumulative dose-volume histograms. Results: Semi-automatic and automatic MRI to CT liver registration achieved comparable Dice scores of 92%. However, tumour registration varied significantly between registration methods yielding average Dice scores of 79%. Multimodal tumour segmentation approaches outperformed monomodal ones, achieving average Dice scores of 66.6 versus 62.5%. Using the baseline pipeline, the average tumour absorbed dose per patient was 115.6 Gy. Using the fully automatic approach, tumour absorbed doses differed from baseline values by an average of 6.6 Gy. Differences ranged from 0.3 Gy in the best case to 202.8 Gy when the automatic tumour segmentation markedly deviated from the manual delineation. Finally, it was found that a Dice score of at least 80% was required to avoid statistically significant differences in absorbed dose estimates between the clinical and automatic approaches. Conclusion: Semi-automatic and automatic registration show equivalent performance, allowing for complete automation. Automatic segmentation demonstrates promising results, with approximately 40% of patients achieving tumour Dice scores above the 80% threshold. 30% of cases show intermediate performance (60–80% tumour Dice scores), while the remaining 30% are still challenging. Further refinement of segmentation methods is required to enhance dosimetric accuracy.