Surgical pathology and sustainable development: international landscape and prospects.

The healthcare sector significantly contributes to global greenhouse gas emissions, with surgical pathology (SP) playing a notable role. This review explores the ecological transformation of SP, offering a global overview of existing challenges and sustainable initiatives worldwide.While some countries, such as the UK and France, have developed national strategies to reduce the carbon footprint of healthcare, including SP, many regions remain at an early stage of implementing green practices.

[The environmental impact of digital technology and artificial intelligence, in the era of digital pathology].

While digitization and artificial intelligence represent the future of our specialty, future is also constrained by global warming and overstepping of planetary limits, threatening human health and the functioning of the healthcare system. The report by the Délégation ministérielle du numérique en santé and the French government's ecological planning of the healthcare system confirm the need to control the environmental impact of digital technology. Indeed, despite the promises of dematerialization, digital technology is a very material industry, generating greenhouse gas emissions, problematic consumption of water and mineral resources, and social impacts.

The carbon footprint of a surgical pathology laboratory in France.

Objectives: The health sector contributes to climate disruption through greenhouse gas (GHG) emissions. It accounts for 8% to 10% of France's GHG emissions. Although the medical community has been alerted to the problem, more data are needed.

Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach.

The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations between centers and the need to estimate visually the staining intensity in specific percentages of tumor area. In this paper, focusing on the interpretability of HER2 scoring by a pathologist, we propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines defined by the American Society of Clinical Oncology/ College of American Pathologists (ASCO/CAP).

A case of feline gastrointestinal eosinophilic sclerosing fibroplasia associated with fungal colonisation: endoscopic features, treatment and follow-up.

Case Summary: A 5-year-old castrated male domestic shorthair cat presented with a 3-month history of weight loss, chronic diarrhoea and vomiting. Examination revealed a large proximal duodenal lesion eventually diagnosed as feline gastrointestinal eosinophilic sclerosing fibroplasia (FGESF) associated with fungal filaments. Histological examination was performed following endoscopic biopsy.

Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?

The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world.

Using multiple images and contours for deformable 3D-2D registration of a preoperative CT in laparoscopic liver surgery.

Purpose: Laparoscopic liver resection is a challenging procedure because of the difficulty to localise inner structures such as tumours and vessels. Augmented reality overcomes this problem by overlaying preoperative 3D models on the laparoscopic views. It requires deformable registration of the preoperative 3D models to the laparoscopic views, which is a challenging task due to the liver flexibility and partial visibility.

Automatic preoperative 3d model registration in laparoscopic liver resection.

Purpose: : Augmented Reality (AR) in Laparoscopic Liver Resection requires anatomical landmarks and the silhouette to be found on the laparoscopic image. They are used to register the preoperative 3D model obtained from CT segmentation. The existing AR systems rely on the surgeon to 1) annotate the landmarks and silhouette and 2) provide an initial registration.

A Partial Label-Based Machine Learning Approach For Cervical Whole-Slide Image Classification: The Winning TissueNet Solution.

Cervical cancer is the fourth most common cancer in women worldwide. To determine early treatment for patients, it is critical to accurately classify the cervical intraepithelial lesion status based on a microscopic biopsy. Lesion classification is a 4-class problem, with biopsies being designated as benign or increasingly malignant as class 1-3, with 3 being invasive cancer.

Mutual cineMR/RT3DUS cardiac segmentation.

We present a new method to segment a cardiac RT3D ultrasound volume by integrating the registered segmentation of a cardiac cine-MR series in short axis of the same patient. The motivation behind our method is to improve the ultrasound segmentation process by integrating a reference shape built using the cine-MR segmentation on the same patient. As a side effect we obtain a close registration of the cine MR short axis slices with respect to the ultrasound volume.

Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging.

This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method.

How big is this neoplasia? live colonoscopic size measurement using the Infocus-Breakpoint.

Colonoscopy is the reference medical examination for early diagnosis and treatment of colonic diseases. This minimally invasive technique allows endoscopists to explore the colon cavity and remove neoplasias - abnormal growths of tissue - which may develop into malignant tumors. The size, shape and appearance of a neoplasia are essential cues for diagnostic.

Enhanced imaging colonoscopy facilitates dense motion-based 3D reconstruction.

We propose a novel approach for estimating a dense 3D model of neoplasia in colonoscopy using enhanced imaging endoscopy modalities. Estimating a dense 3D model of neoplasia is important to make 3D measurements and to classify the superficial lesions in standard frameworks such as the Paris classification. However, it is challenging to obtain decent dense 3D models using computer vision techniques such as Structure-from-Motion due to the lack of texture in conventional (white light) colonoscopy.

Measuring the size of neoplasia in colonoscopy using Depth-From-Defocus.

Colonoscopy is the reference medical examination for the diagnosis and treatment of neoplasia in gastroenterology. During the examination, the expert explores the colon cavity with a gastroscope in order to detect neoplasias - abnormal growths of tissue - and to diagnose which ones could be malignant. The Paris classification of superficial neoplastic lesions is the gold standard set of criteria for this type of diagnosis.

Nonsupervised ranking of different segmentation approaches: application to the estimation of the left ventricular ejection fraction from cardiac cine MRI sequences.

A statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eight methods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical distribution of the parameter of interest in the database.

Comparison of different segmentation approaches without using gold standard. Application to the estimation of the left ventricle ejection fraction from cardiac cine MRI sequences.

A statistical method is proposed to compare several estimates of a relevant clinical parameter when no gold standard is available. The method is illustrated by considering the left ventricle ejection fraction derived from cardiac magnetic resonance images and computed using seven approaches with different degrees of automation. The proposed method did not use any a priori regarding with the reliability of each method and its degree of automation.

Monitoring of polyethylene wear in nonmetal-backed acetubular cups by digitized anteroposterior pelvic radiography.

The aim of this study was to assess polyethylene wear in a total hip prosthesis by digitized radiography of the whole pelvis in the anteroposterior (AP) plane. The three-dimensional (3-D) pose of the nonmetal-backed acetubular cup, materialized by its metal ring and the femoral head made of metal or ceramic, was estimated using iterative algebraic algorithms with inner bias correction and bootstrapping for variance reduction. Points of interest were obtained by maximizing the correlation between sampled density profiles and 3-D geometric models degraded by the modulation transfer function (MTF) of the radiographic system and the film scanner.