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Most image segmentation algorithms are trained on binary masks formulated as a classification task per pixel. However, in applications such as medical imaging, this "black-and-white" approach is too constraining because the contrast between two tissues is often ill-defined, i.e., the voxels located on objects' edges contain a mixture of tissues (a partial volume effect). Consequently, assigning a single "hard" label can result in a detrimental approximation. Instead, a soft prediction containing non-binary values would overcome that limitation. In this study, we introduce SoftSeg, a deep learning training approach that takes advantage of soft ground truth labels, and is not bound to binary predictions. SoftSeg aims at solving a regression instead of a classification problem. This is achieved by using (i) no binarization after preprocessing and data augmentation, (ii) a normalized ReLU final activation layer (instead of sigmoid), and (iii) a regression loss function (instead of the traditional Dice loss). We assess the impact of these three features on three open-source MRI segmentation datasets from the spinal cord gray matter, the multiple sclerosis brain lesion, and the multimodal brain tumor segmentation challenges. Across multiple random dataset splittings, SoftSeg outperformed the conventional approach, leading to an increase in Dice score of 2.0% on the gray matter dataset (p=0.001), 3.3% for the brain lesions, and 6.5% for the brain tumors. SoftSeg produces consistent soft predictions at tissues' interfaces and shows an increased sensitivity for small objects (e.g., multiple sclerosis lesions). The richness of soft labels could represent the inter-expert variability, the partial volume effect, and complement the model uncertainty estimation, which is typically unclear with binary predictions. The developed training pipeline can easily be incorporated into most of the existing deep learning architectures. SoftSeg is implemented in the freely-available deep learning toolbox ivadomed (https://ivadomed.org)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantification requires the segmentation of these structures into three separate classes. However, manual segmentation of three-dimensional structures is time consuming, tedious and prone to intra- and inter-rater variability, motivating the development of automated methods. Here, we tailor a model adapted to the spinal cord tumor segmentation task. Data were obtained from 343 patients using gadolinium-enhanced T1-weighted and T2-weighted MRI scans with cervical, thoracic, and/or lumbar coverage. The dataset includes the three most common intramedullary spinal cord tumor types: astrocytomas, ependymomas, and hemangioblastomas. The proposed approach is a cascaded architecture with U-Net-based models that segments tumors in a two-stage process: locate and label. The model first finds the spinal cord and generates bounding box coordinates. The images are cropped according to this output, leading to a reduced field of view, which mitigates class imbalance. The tumor is then segmented. The segmentation of the tumor, cavity, and edema (as a single class) reached 76.7 ± 1.5% of Dice score and the segmentation of tumors alone reached 61.8 ± 4.0% Dice score. The true positive detection rate was above 87% for tumor, edema, and cavity. To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation. The multiclass segmentation pipeline is available in the Spinal Cord Toolbox (https://spinalcordtoolbox.com/). It can be run with custom data on a regular computer within seconds., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The authors report on an autopsy-proven case of multifocal widespread radionecrosis involving both cerebral hemispheres and masquerading as tumor dissemination on a CT scan done 13 months after complete resection of an oligodendroglioma followed by radiation therapy. This case demonstrates that radiation damage may be present in a CT scan as a multifocal, disseminated lesion. Since the survival of brain-tumor patients who have undergone radiation therapy is prolonged by aggressive therapy, the incidence and variability of radiation-induced complications in such cases is likely to increase. For similar reasons, the radionecrosis in such cases should be taken into consideration. A short review of the CT scan findings and diagnostic and therapeutic considerations in a case of widespread radionecrosis is presented. The need for appropriate diagnosis and subsequent life-saving management is emphasized.

In a clinical trial, 10 patients with malignant gliomas underwent partial resection of their tumors and were treated by intraarterial 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU) chemotherapy. The drug was given at least 1 month after the completion of postoperative radiotherapy in total doses of 270-280 mg/m2 in two sessions separated by a 48-hour interval (the two sessions with interval were equal to one course). This therapy was repeated every 8-10 weeks. Four patients had three courses and the other six patients had two courses of chemotherapy. This therapy was the only antitumor chemotherapy for this group of patients. Our preliminary results demonstrate the safety of this new procedure and suggest that intraarterial BCNU chemotherapy may be more effective, and has a better tolerance and less toxicity, than intravenous BCNU chemotherapy. Furthermore, it was demonstrated that, in the case of one patient, higher antitumor activity resulted from intraarterial BCNU chemotherapy as compared to intravenous BCNU therapy.